Rafael Alexandrou

Harris Papadopoulos

Andreas Konstantinidis

Indoor Localization with Multi-Objective selection of Radiomap Models

Keywords: Indoor Localization, Smartphones, Fingerprinting, Artificial Neural Networks, Multi-Objective Optimization

Over the last years, Indoor Localization Systems (ILS) evolved, due to the inability of Global Positioning Systems (GPS) to localize in indoor environments. A variety of studies tackle indoor localization with technologies such as Bluetooth Beacons and RFID that require costly installation, or techniques such as Google Wi-Fi/Cell DB and fingerprinting that leverage from the already existing Wi-FI and telecommunication infrastructure. Additionally, recent studies attempt to solve the same problem using Bio-Inspired techniques, such as Artificial Neural Networks (ANNs) and Deep Neural Networks (DNN). In this paper, we introduce a Multi-Objective Optimization Radiomap Modelling (MOO-RM) based ILS. The MOO-RM ILS divides the dataset into clusters using a K-Means algorithm and trains ANN models on the data of each cluster. The resulting models are fed into a Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D), which minimizes the required storage space and the localization error, simultaneously. Our experimental studies demonstrate the superiority of the proposed approach on real datasets of Wi-Fi traces with respect to various existing techniques.

Introduction

Currently, Global Navigation Satellite Systems (GNSS), such as GPS, are unable to localize accurately in indoor environments due to the satellite signal attenuation while passing through solid objects. This has led to an increased interest for alternative localization techniques by the scientific community. In particular, the indoor localization community has proposed a variety of solutions that include technologies such as Bluetooth Beacons (BLE), Infrared, Li-Fi technologies, RFID, Sensor Networks and their combinations, for localizing a device in an indoor environment with a fine-grain accuracy [START_REF] Gu | A survey of indoor positioning systems for wireless personal networks[END_REF]. These techniques, however, require the deployment of specialized equipment such as antennas, beacons, and custom transmitters a priori [START_REF] Peter | An overview of indoor localization technologies and applications[END_REF], which is time consuming and costly.

In order to alleviate the aforementioned challenges, a variety of Indoor Localization Systems (ILS) that rely on geolocation data retrieved from the existing infrastructure of a building, such as wireless signals, have been implemented. These ILS, such as Google, Indoo.rs, Navizon, IndoorAtlas, ByteLight and Anyplace1 [START_REF] Petrou | Crowdsourced indoor localization and navigation with anyplace[END_REF] have managed to provide accurate indoor localization of a user without the need of any additional hardware.

Geolocation database entries act as reference points for the localization tasks. A comparison between those reference points and a sensed point from a smartphone device, either on the service (Server-Side) or the smartphone itself (Client-Side), can help determine a user's location within an area. As [START_REF] Konstantinidis | Privacy-preserving indoor localization on smartphones[END_REF] suggests, Server-Side violates user's privacy, since calculations, that are processed on the server, reveal the actual location of a user to the service. Contrary, such concern does not apply for Client-Side, since the service only knows the area that surrounds the user and not the user's actual location. However, since the main processing of the ILS is done on the smartphone, this leads to performance concerns, mainly, due to the vast amount of data being downloaded and processed locally.

In this paper, we examine the possibility of providing the user with a variety of solutions based on his preferences, by introducing a novel Multi-Objective Optimization Radiomap Modelling (MOO-RM) based ILS. The solutions include Artificial Neural Network (ANN) models, associated to clusters of location data, which are generated using K-Means and can be used to localize users. Then, a Multi-Objective Evolutionary Algorithm Based on Decomposition (MOEA/D) is used to minimize the storage space requirements on the smartphone device and the localization error, by selecting a near-optimal set of models from the set of all models that represent the whole location-based dataset.

The rest of this paper is organized as follows. Section 2 covers the background of indoor localization and provides solutions in the literature that are similar to our proposed method. Section 4 presents the proposed ILS method, which is evaluated in Section 5. Finally, Section 6 discusses future work.

Related Work

In this section, background on indoor localization that lies at the foundations of the proposed approach is introduced.

Indoor Localization with Smartphones

A wide range of technologies dealing with localization in outdoor and indoor environments is provided in the literature. For outdoor environments, Global Navigation Systems (GNSS), such as GPS and Galileo, are considered the leading technologies for localization. However, they require high energy consumption and their signal attenuation while passing through solid objects, such as concrete walls, is negatively affecting navigation in indoor environments. A variety of indoor localization solutions [START_REF] Gu | A survey of indoor positioning systems for wireless personal networks[END_REF] have been proposed including technologies such as BLE sensors, visual or acoustic analysis, RFID, Wireless Sensor Networks, laser and LiFi, IMUs and their combinations into hybrid systems. Even though the performance of the aforementioned suggest positive votes, most of these techniques require the costly deployment of additional equipment a priori to localization. Contrary, the indoor localization community introduced localization approaches, such as Cell/Wi-Fi Database and Wi-Fi Fingerprinting, that rely on existing Wi-Fi infrastructure already deployed in most buildings.

Wi-Fi Fingerprints construct a database of radio signals from Wi-Fi Access Points (Wi-Fi APs) within the area. In the literature, Anyplace [START_REF] Zeinalipour-Yazti | The anatomy of the anyplace indoor navigation service[END_REF][START_REF] Georgiou | Anyplace: A crowdsourced indoor information service[END_REF][START_REF] Laoudias | The airplace indoor positioning platform for android smartphones[END_REF] achieved the second highest known accuracy [START_REF] Lymberopoulos | A realistic evaluation and comparison of indoor location technologies: Experiences and lessons learned[END_REF], with an average error of 1.96 meters. In particular, Anyplace is divided into two phases: The offline phase, or "logging" phase, records the Wi-Fi Fingerprints, which are Received Signal Strength indications (RSSi) of Wi-Fi APs at certain locations (x, y) of a building, into an N ×M matrix known as a radiomap. The radiomap itself consists of N unique fingerprints and M Wi-Fi APs. In the online phase, or "localization" phase, a smartphone user observes the RSSi from the surrounding Wi-Fi APs and compares it against the radiomap using either K-Nearest Neighbors (KNN) or Weighted K-Nearest Neighbors (WKNN) to find the best match. Finally, Anyplace provides two different approaches: In Server-Side Fingerprinting (SS), the main localization process happens on a server that has unlimited energy, storage and processing budget. Therefore, the smartphone user requires little network messaging and minimal energy consumption to localize. However, this approach violates the user's privacy, since the server knows the actual location of the user. Contrary, Client-Side Fingerprinting (CS) focuses on processing locally on the smartphone to eliminate the privacy concerns. However, this leads to high resource consumption on the client device.

In our previous work [START_REF] Alexandrou | Smartphone Indoor Localization Using Bio-inspired Modeling[END_REF], we proposed a CS fingerprinting approach that utilizes Artificial Neural Networks (ANNs) to train a model representing the whole radiomap data. Even though this approach reduces the energy consumption and storage space required on the smartphone, while preserving user's privacy and maintaining an acceptable localization error compared to conventional fingerprinting techniques, we realized that storage space requirements are still high for smartphone devices. In this work, we investigate the trade-off between storage space requirements and localization error by providing multiple solutions, that consist of a subset of ANN models corresponding to clusters within the radiomap. A variety of solutions will allow users to localize in indoor environments based on their preferences (e.g. preserve battery/storage levels on the device, or minimize localization error during localization).

Smartphone Indoor Localization Applications

In [START_REF] Félix | A fingerprinting indoor localization algorithm based deep learning[END_REF], authors present a deep learning-based fingerprinting localization schema. Similarly, [START_REF] Wang | Csi-based fingerprinting for indoor localization: A deep learning approach[END_REF] presents a deep-learning approach that utilizes deep architectures and Channel State Information (CSI) on fingerprinting for localization. Additionally, authors of [START_REF] Abbas | Wideep: Wifibased accurate and robust indoor localization system using deep learning[END_REF] present the WiDeep approach that uses a probabilistic denoising auto-encoder. All three aforementioned research studies provide solutions for improving the fingerprinting technique. However, the use of deep neural networks requires high volume of resources during localization, due to networks' structure complexity.

Furthermore, the indoor localization problem is, also, tackled as an optimization problem, in the literature. An indoor localization problem is formulated in [START_REF] Bergenti | A bio-inspired approach to wifi-based indoor localization[END_REF] and optimized with a Particle Swarm Optimization (PSO) approach, named JADE. Likewise, [START_REF] Ding | Indoor fingerprinting localization and tracking system using particle swarm optimization and kalman filter[END_REF] proposes a fingerprinting localization and tracking system with PSO and Kalman Filter(KF). Finally, the authors of [START_REF] Pericleous | A multi-objective indoor localization service for smartphones[END_REF] present MI-Los, a multi-objective indoor localization service that utilizes a Multi-Objective Evolutionary Algorithm Based on Decomposition (MOEA/D) to maximize the coverage and minimize the energy consumption at the same time. The latter optimizes coverage and energy consumption objectives on conventional fingerprinting techniques, whereas our proposed method optimizes the storage space required on the device and the localization error using ANN models, which are clustered using K-Means.

3 System Overview

System Model

We assume an area A divided into several building areas A 1 , .., A n , which consist of several floors F 1 , .., F m (see Figure 1). Each A i and its corresponding F j contain a finite set of (x, y) points and is covered by a set of Wi-Fi Access Points {ap 1 , ap 2 , • • • , ap M }, each covering a planar points. Area A i is not necessarily continuous and can be considered as the joint area of all ap i ∈ AP (i.e., global coverage). Each ap i has a unique ID (i.e., MAC address) that is publicly broadcasted and passively received by anyone moving in the a points of ap i . The signal intensity at which the ID of ap i is received at location (x, y), is termed the Received Signal Strength (RSS) of ap i at (x, y), having a value in the range [-30... -110]dB.

Let a static (cloud-based) localization service s hosting an N × M table, coined RadioMap (RM ), which records the RSS of the ap i ∈ AP broadcasts at specified (x, y) ∈ A locations. When an ap i is not seen at a certain (x, y) the RM records "-110" in its respective cell. A user u localizes through the indoor positioning service s, using the ID and RSS broadcasts of surrounding ap i ∈ AP while moving. This information is termed, hereafter, RSS Vector or Fingerprint (V u ) of user u, which changes from location to location and over time. Contrary to RM rows having M attributes, V u has only M M attributes.

Research Goal & Metrics

Research Goal. Allow a smartphone user to localize with fine accuracy and minimum storage resources by selecting a subset of ANN models representing clusters of Radiomap data.

The efficiency of the proposed technique to achieve the above research goal is measured by the Localization Error, and the Storage Space Requirements on the client device u. The Localization Error is defined as the Haversine distance (in m), between the predicted location (x 2 , y 2 ) and the actual location (x 1 , y 1 ) of u,

d = 2r × arcsin( sin 2 ( x 2 -x 1 2 ) + cos(x 1 ) cos(x 2 ) sin 2 ( y 2 -y 1 2
))/1000 [START_REF] Gu | A survey of indoor positioning systems for wireless personal networks[END_REF] where r = 6371 km is the radius of earth.

Storage Space (Size s ) is the storage space required on the client device in order to perform indoor localization.

Proposed Methodology

Our proposed Indoor Localization Method is divided into four phases, namely, Data Clustering, Modeling, Optimization, and Decision Making. Each phase is explained next.

Data Clustering

Data Clustering utilizes a K-Means algorithm to divide the dataset into clusters.

In particular, K-Means is a vector quantization method that is widely used in data mining and aims to partition n observations into k clusters based on their distances.

In our proposed method, K-Means is applied to the multi-dimensional fingerprinting dataset for forming clusters with respect to the whole vector, considering both the sensed Received Signal Strength Intensity (RSSI) and the mapped geolocation. Figure 2 provides an example of the resulting clusters for the three floors of our dataset.

Modeling

After the clustering process, each cluster is modelled with an Artificial Neural Network (ANN). In particular, the trained models are able to predict the user's current location based on a vector V u that contains the RSSIs of the user's surrounding Wi-Fi APs, arranged in the same order for all patterns. The models used were fully connected feed-forward ANNs with sigmoid hidden and linear output activation functions and were trained with a stochastic gradient descent optimizer called Adam. Implementation was performed using the Scikit-Learn library in a Python environment with the ANN regression algorithm MLPRegressor. Following a trial and error approach for the network's structure led to the average optimum scenario of 100 hidden units on a single hidden layer. The trained models were extracted and stored on the server in order to be readily available for being sent to a smartphone device. The model extraction was performed manually since Scikit-learn uses a python tool to extract models commonly known as pickle( or its updated version called joblib) which extracts unnecessary information along with the model. This lead to a larger model file compared to the original RM s, which conflicts with one of the major goals of the proposed method i.e. to minimize the storage space required on the smartphone device. Therefore, the model's coefficients and intercepts were extracted and saved into two small files. On the smartphone device, both files were loaded and used to reconstruct the model locally, before localization. Note that this reconstruction does not imply re-training the model, since the coefficients and intercepts are known from the training phase.

Multi-Objective Optimization

A Multi-objective Optimization Problem (MOP) can be mathematically formulated as minimize

F (X) = (f 1 (X), ..., f k (X)), subject to X ∈ Ω ( 2
)
where Ω is the decision space and X ∈ Ω is a decision vector. F (X) consists of k objective functions f i : Ω → R, i = 1, ..., k, where R k is the objective space. The objectives often conflict with each other and improving on one objective may lead to deterioration of another. Thus, no single solution exists that can optimize all objectives simultaneously. In that case, the best trade-off solutions, called the set of Pareto optimal (or non-dominated) solutions, is often required by a decision maker. The image of the Pareto Set (PS) in the objective space is called the Pareto Front (PF).

In this paper the MOP aims at minimizing the storage space required on the smartphone device and the localization error, and it is tacked by the Multi-Objective Evolutionary Algorithm Based on Decomposition (MOEA/D). The initial population is generated by a random generator that provides binary vectors, of length equal to number of clusters, where 1 and 0 denote that the corresponding model is included to the set or not. Finally, MOEA/D follows a single point crossover and the termination condition is set to n maximum generations.

Decision Making

Decision Making is the last phase and has access to a set of solutions instead of a single one. It relies on the user's input and the smartphone's available storage. Considering a device that has limited storage availability, the user is asked to decide whether he/she wants to save storage by sacrificing localization accuracy. The user's choice will dictate the need for a more accurate or less storage-dependent model subset. Finally, note that the less storage-dependent model subset implies reduced accuracy within the whole area but not within the part of the area that is covered by the subset. (e.g. model subsets that include only 1 model will be able to predict user's location accurately in 14% of the whole area).

Experimental Evaluation

Dataset

For our experimental evaluation, we used a real dataset consisting of ≈ 45,000 reference fingerprints taken from ≈ 120 Wi-Fi APs installed in three floors of a building in Cyprus. Therefore, it contains three radiomaps that correspond to the three floors of the building. Firstly, each floor's radiomap was divided into smaller clusters, which further divided into a training and a test set corresponding to 80% and 20% of the data, respectively. The training sets were used to model each cluster's data and the models were evaluated in the corresponding test sets.

The proposed indoor localization method is evaluated in terms of Localization Error and Storage Space required on the device, as defined in Section 3.2.

Evaluation Metrics

The metrics utilized during the optimization phase are defined as follows:

Hypervolume (I H ) indicates the area dominated by at least one solution in an obtained non-dominated set A. Therefore, high I H suggests better diversity. The metric is formally defined as

I H (A) = x∈U x∈A ... HV (f (x),f * ) 1.dz (3)
where C-Metric is defined by the percentage of non-dominated solutions of set A with respect to solutions of a non-dominated set B and can be annotated as C(A, B).

HV (f (x), f * ) = [f 1 (x), f * 1 ] × ... × [f m (x), f * m ] is the Cartesian product of the closed intervals [f i (x), f * i ], i =

Experimental Study 1: Number of Clusters

In the first experimental study, we evaluated the K-means algorithm for various cluster sizes in terms of localization error. Note that various cluster sizes were examined but only three are presented, which are representative for all cases.

Mainly, the focus of this experimental study is to provide information regarding the expected output of the whole set of models. Figure 3 shows the comparison of three cluster sizes, in terms of localization error and storage space requirements. Figure 3a shows a similar average localization error of clusters for the three different clusters sizes at each floor. Figure 3b examines the storage space requirements on the smartphone device and shows that for floor 0 and floor 2 are similar in all three cases. Contrary, floor 1 results show that the lowest storage space required is achieved using seven clusters. In general, seven clusters seem to provide promising results, for both objectives, and therefore it is adopted in all experimental studies that follow.

Experimental Study 2: Optimization Integrity

The second experimental study focuses on ensuring the integrity of MOEA/D while minimizing the two conflicting objectives with respect to the state-of-theart in MOO, Non-Dominated Sorting Genetic Algorithm II (NSGA-II). Table 1 shows a comparison between NSGA-II and MOEA/D in terms of the metrics defined in Section 5.2. The results show similar performance with respect to I H and C, but it seems that MOEA/D provides a higher number of N DS to the decision maker.

Experimental Study 3: Localization Error and Storage Space

In order to ensure the usability of the proposed indoor localization method, we evaluate our models with the floor level test set obtained in Section 4.2 and over three randomly generated paths within the area, namely, a visitor, a professor, and a student path. Finally, our proposed method was evaluated over an error-dependent scenario (solution with minimum localization error), a storage-dependent scenario (solution with minimum storage space required), a conventional fingerprinting technique such as Weighted K-Nearest Neighbors (WKNN), and an Artificial Neural Network (ANN) model generated from the whole training set (without clustering).

Figure 4 provides the Pareto Front (PF) solutions obtained from the optimization phase. Additionally, the solutions of WKNN and ANN single model are provided within the same figure for each floor, as baselines for minimum localization error and minimum storage requirements, respectively. As shown in Figure 4a, for floor 0, all MOO-RM PF solutions require less storage space than WKNN, and 71% of the MOO-RM PF solutions require less storage space than the ANN model, as well. As expected, since the WKNN solution is one of our baselines, it has the minimum localization error, whereas the majority of MOO-RM PF solutions are similar to the localization error of the ANN. Figure 4b shows that all MOO-RM PF solutions require less storage than WKNN and 64% of MOO-RM PF need less storage space than the ANN, for floor 1. Additionally, various MOO-RM PF solutions are close to the localization error provided by the ANN. Lastly, Figure 4c represents the MOO-RM PF solutions for floor 2. The results demonstrate the superiority of the proposed approach in real life large scale datasets. In particular, all MOO-RM PF solutions require less storage space compared to the WKNN and ANN baselines. Additionally, there are solutions that are similar to WKNN's localization error with a significant decrease in storage space requirements.

Figure 5 shows the results on the user defined paths using the two extreme solutions in the PF obtained by MOO-RM that is: i) Error-Dependent solution provides the least possible localization error without any storage space concerns, ii) Storage-Dependent solution provides the least possible storage space on the smartphone device without considering the localization error levels. The results demonstrate that in almost all cases, MOO-RM maintains an acceptable localization error and less storage space requirements, compared to WKNN and ANN when considering the Error-Dependent solution. Additionally, the Storage-Dependent solution provides a localization error, which is higher compared to WKNN and ANN, offering however a storage space decrease of 99%.

Experimental Study 4: Building Level

In this experimental study, the proposed MOO-RM was evaluated over a building level dataset. In particular, we utilized all 21 models of the three floors, and we examined the performance of MOO-RM on a building level (larger dataset) than on a floor level.

The comparison between MOO-RM, the conventional fingerprinting (WKNN), and the ANN model is shown in Table 2. In particular, the proposed method has an increase of 6% in terms of localization error and minimizes the storage space by 56%, compared to WKNN. Additionally, MOO-RM decreases the localization error by 54% and the storage space requirements by 67%, compared to the ANN single model. The ANN model attempts to predict the radiomap of the whole building area A, while MOO-RM utilizes a MOO selection between the 21 clusters converging to better localization error. In this paper, a novel MOO-RM-based ILS is introduced. MOO-RM provides the user with a variety of solutions in different granularities, each providing different levels of localization error and storage space requirements. This allows the user to localize in an indoor environment based on his preferences e.g. limited storage availability levels might require a smaller model with high localization error trade-off. The experimental results demonstrate the superiority of the proposed approach when compared with conventional client-side and ANN fingerprinting approaches, in terms of resource consumption, while preserving user privacy and maintaining localization accuracy.

Finally, this research study provides solutions based on a fixed ANN structure. As a future challenge, we would like to examine the use of variable structures between the cluster models provided to the MOEA/D. In particular, different ANN model structures result in different levels of storage space requirements and localization error. This will allow us to provide better solutions to smartphone users while navigating based on their preferences.

Fig. 1 :

 1 Fig. 1: System Model

Fig. 2 :

 2 Fig. 2: Geographical Representation of Clusters

1 ,

 1 .., m. Since we consider minimization objectives the reference point f * = (f * 1 ...f * m ) is the ideal worst point. Number of Non-Dominated Solutions (N DS) is the number of non-dominated solutions in set A. N DS(A) = |A| (4)

Fig. 3 :

 3 Fig. 3: Clusters Performance

Fig. 4 :

 4 Fig. 4: Floor Level Evaluation

  Fig. 5: Paths Comparison

Table 1 :

 1 NSGA-II(A) vs MOEA/D(B)

	Floor IH (A) N DS(A) C(A, B) IH (B) N DS(B) C(B, A)
	0	0.846	7	0.571	0.819	9	0.666
	1	0.778	14	0.714	0.834	22	0.545
	2	0.73	14	0.8	0.73	20	0.8

Table 2 :

 2

	Building Level Comparison
		WKNN ANNs MOO-RM
	Error(m)	4.274	9.96	4.55
	Storage(Kb)	2729	3697	1203

Available at: http://anyplace.cs.ucy.ac.cy/

Acknowledgements

This work is part of the "EnterCY" Integrated Project with project number INTEGRATED/0609/0020, which is co-funded by the European Regional Development Fund and the Cyprus Government through the Research & Innovation Foundation (RIF) program RESTART 2016-2020.


Georgios Alexandridis
email: gealexandri@aegean.gr

Konstantinos Michalakis
email: kmichalak@aegean.gr

John Aliprantis

Pavlos Polydoras

Panagiotis Tsantilas

George Caridakis

A deep learning approach to aspect-based sentiment prediction

Keywords: aspect-based sentiment analysis, bi-directional long shortterm memory units, convolutional neural networks, attention mechanism, deep learning

Sentiment analysis is a vigorous research area, with many application domains. In this work, aspect-based sentiment prediction is examined as a component of a larger architecture that crawls, indexes and stores documents from a wide variety of online sources, including the most popular social networks. The textual part of the collected information is processed by a hybrid bi-directional long short-term memory architecture, coupled with convolutional layers along with an attention mechanism. The extracted textual features are then combined with other characteristics, such as the number of repetitions, the type and frequency of emoji ideograms in a fully-connected, feed-forward artificial neural network that performs the final prediction task. The obtained results, especially for the negative sentiment class, which is of particular importance in certain cases, are encouraging, underlying the robustness of the proposed approach.

Introduction

Sentiment analysis or opinion mining has become a vigorous research area, especially in recent years, with the vast expansion of the world-wide web and the proliferation of online social networks (OSNs), like Facebook, Twitter and Instagram. Indeed, people discuss, voice opinions, share digital content and generally engage in activities, in a large public space. This reality has caught the attention of businesses and organizations, whose objective is to study and analyze public opinion with respect to the products and services they offer. Ideally, the aforementioned parties need not conduct surveys or opinion polls any more, as there is an abundance of relevant information available online.

However, locating and extracting user opinion from online sources (social media sites, blog posts, forums, etc) is a rather cumbersome task. Apart from the huge volume of information that needs to be processed, one has to be familiarized with the specifics of each service (e.g. API calls) and with the sentiment annotation processes. Therefore, it is not uncommon for companies to resort to specialized analysts that offer content services for consumers and brands.

From the business analyst perspective, sentiment analysis is a multi-faceted task. In [START_REF] Liu | Sentiment Analysis and Opinion Mining[END_REF], three distinct levels of analysis are identified; (i) document, (ii) sentence, (iii) entity and aspect. At the first level, a single sentiment is assigned on the whole document (e.g. positive or negative). This is practical for sources like news agencies, that usually discuss only one entity. At the second level of analysis, sentiment is extracted on a per sentence basis, having application on documents discussing more than one entities or on micro-blogging platforms like Twitter, where documents commonly consist of a few sentences.

The third level of analysis is the most demanding task, as instead of examining language constructs, the emphasis is placed on the entity or the aspect level. For example, a tweet stating "Company X offers a great service, Thank God I switched over from Company Y" can be classified as positive, w.r.t. Company's X service, negative w.r.t to Company Y and neutral, w.r.t. other similar companies. Therefore, the same text excerpt may have different interpretations. Additionally, subjective criteria may arise when deciding upon opinion or sentiment; for instance, a business may consider the reproduction of one of its press releases by a news agency a positive event, while another may view this event as neutral.

In this work, sentiment prediction is modelled as a supervised classification problem and is addressed using a deep learning architecture, based on Bidirectional Long Short-Term Memory (BiLSTM) units [START_REF] Graves | Bidirectional lstm networks for improved phoneme classification and recognition[END_REF], combined with convolutional attention layers [START_REF] Raffel | Feed-forward networks with attention can solve some long-term memory problems[END_REF]. More specifically, Section 2 discusses related work and Section 3 presents the overall system architecture. Section 4 describes the data collected by the system, while Section 5 presents the feature extraction procedure, the implemented model and the obtained results. Finally, the work concludes in Section 6.

Related Work

Even though the research areas of sentiment analysis and opinion mining firstly appeared in 2003 [START_REF] Dave | Mining the peanut gallery: Opinion extraction and semantic classification of product reviews[END_REF], a multitude of works have been published on the subject ever since [START_REF] Liu | Sentiment Analysis and Opinion Mining[END_REF], based on various methodologies [START_REF] Ravi | A survey on opinion mining and sentiment analysis: Tasks, approaches and applications[END_REF]. Nevertheless, in recent years, most state-of-the-art approaches are related to deep learning techniques. For example, the key element of the proposed system in [START_REF] Glorot | Domain adaptation for largescale sentiment classification: A deep learning approach[END_REF] (studying the domain adaptation problem for sentiment classification) is a stacked denoising autoencoder that performs unsupervised feature extraction using both labeled and unlabeled samples. In [START_REF] Tang | Document modeling with gated recurrent neural network for sentiment classification[END_REF], a neural network consisting of convolutional and LSTM layers is being presented, that learns document representations by considering sentence relationships. Other works combine the use of LSTMs with attention mechanisms; for instance, in [START_REF] Yin | Document-level multi-aspect sentiment classification as machine comprehension[END_REF], the document-level aspect-sentiment rating prediction task is formulated as a comprehension problem that is being addressed by a hierarchical interactive attention-based model.

LSTMs have also been used in aspect level sentiment classification. In [START_REF] Tang | Effective lstms for target-dependent sentiment classification[END_REF], the target-dependent and target-connection extensions to LSTM are proposed. The target is considered as another input dimension and is subsequently concatenated with the other features. A similar approach is followed in the current work; however, in the proposed methodology bi-directional LSTMs are employed instead. Bi-directional LSTMs with word embeddings at their input are used in [START_REF] Ruder | A hierarchical model of reviews for aspectbased sentiment analysis[END_REF] for aspect level sentiment classification, without an attention mechanism though.

An attention-based LSTM methodology for aspect-based sentiment analysis is described in [START_REF] Wang | Attention-based lstm for aspect-level sentiment classification[END_REF], where the attention mechanism has been found to be effective in enforcing the model to focus on the important parts of each sentence, with respect to a specific aspect. Finally, in [START_REF] Yang | Attention based lstm for target dependent sentiment classification[END_REF], two attention-based bidirectional LSTMs are proposed; however, unlike our approach, no other input features are considered apart from text.

System Architecture

The overall system architecture is depicted in Figure 1. It begins with data crawling, the process of systematically accessing a disparate set of online sources, gathering data that satisfy certain filtering criteria and forwarding them into a data repository. The set of data sources being accessed includes traditional web sources such as news sites, blogs, forums, as well as OSNs. There is a constantly updating registry of specific access points per medium, limiting search space only to relevant sources.

Each data source is correlated with potentially multiple data formats, depending on the information granularity. For instance, YouTube exposes hierarchical information, starting from a channel, drilling into metrics (followers and likes), its videos and finally video comments and reactions. The crawling process of this hierarchy needs to be addressed by the corresponding crawler, both in terms of data navigation and crawling policy. The latter is strongly related to data "freshness" (i.e. breaking news need to be crawled as fast as possible), as well as importance from a business perspective (e.g. more popular Instagram accounts need to be crawled more often). The crawling process, directed by these factors, pushes the accessed data sets to the ingestion services, in a streaming manner and re-iterates.

The subsequent step is data cleansing & homogenization. During this process, data are being stripped off inconsistencies attributed to major errors (e.g. missing article date), garbage information injection (e.g. ads in articles) and even erroneous semantics, such as out-of-scope or inappropriate content. Finally, data are stored in the data lake [START_REF] Research | Putting the data lake to work: A guide to best practices[END_REF], a logical database which is the major hub of information exchange among the services. At its final version, the original raw data unit is upscaled into a mention, supplemented by derived information including named entities, image/pattern recognition measures, as well as sentiment indication. Mention semantics extend to a broader definition and usage context which includes a specific domain (e.g. telecommunications) and intended usage (e.g. competition analysis). Naturally, one document may correspond to multiple mentions, each associated with a different semantic context.

Annotation

Sentiment annotation [START_REF] Liu | Sentiment Analysis and Opinion Mining[END_REF] is the process of assigning specific sentiment values to a given mention. Currently, this process considers three values; namely positive, negative and neutral, but it can be generalized to a more extensive set. Orthogonal to sentiment assignment per se, however, is the knowledge base according to which a specific sentiment value can be extracted from a given mention. These rules can be arbitrarily chosen, based on specific criteria, driving sentiment analysis outcome accordingly.

In principle, sentiment annotation criteria are either global or specific, with the former referring to commonly agreed criteria, such as association of negative sentiment and lists of insulting words or phrases. The latter refer to specific rules, which may contradict the global ones and in those cases, they take precedence. The unit of focus is the aspect of the given mention being examined by the rule. Aspects include the text of the mention and its metadata, which in turn consist of the respective data source information (e.g. news site and related category), entity type (e.g. Facebook post or comment), generation time (e.g. twitter comments posted after midnight), author, etc. In all cases, the aspect is well-defined prior to the annotating process and it is uniquely identified by a respective identifier (aspect id).

As stated above, one raw data record is associated with potentially multiple mentions, each corresponding to a different perspective. This results to possibly multiple sentiment values for the same record (remember the discussion about the different interpretations of the same tweet in Section 1), which are determined manually by a human annotator, studying the defined rules and applying them by assigning sentiment values to automatically selected samples. Sample selection follows the stratified random sampling methodology [START_REF] Särndal | Model assisted survey sampling[END_REF], with subgroups defined by the respective data sources (sites, blogs, social media, etc). Annotation is software-assisted and forms the respective data set (Section 4).

Data

Based on the procedure discussed above, 343, 956 Greek language documents have been crawled and annotated over a period spanning nearly 2 years (September 2017 to June 2019). Table 1 outlines the distribution of their sources. As it is evident, the various sources are not evenly represented in this dataset because of the compliance of the crawling procedure to data protection regulations, as discussed in Section 3. For this reason, only public and business accounts are being processed and since Twitter is the most popular OSN in which information is disseminated predominately publicly, it is over-represented. The same reasoning is applied to news sources as well, as the vast majority of Greek news agencies and outlets are being monitored and indexed on a daily basis. Table 2 displays the frequency of appearance of specific domains within the crawled data. More than half of the collected information is about telecommunication businesses (mobile phone operators, Internet service providers, etc), followed by tobacco companies (about 20%). Another interesting observation is the relatively large number of documents related to political parties and politicians, attributed to the fact that 2019 has been an election year in Greece. It should also be noted that the appearance of each specific domain is not evenly spread across all sources (that is, according to the distribution of Table 1). For example, information about the banking sector is predominately collected from news outlets (∼ 70%, when news articles constitute a quarter of the dataset), while politics appear evenly on Twitter and on news articles (∼ 50% and ∼ 40%, respectively). Finally, Table 3 summarizes the distribution of the three categories of annotated sentiment (Section 3.1) over the whole dataset. In total, fiver persons participated in the annotation task, all of whom had received special training on annotation guidelines. Additionally, to further eliminate bias in the labels, well-defined annotation rules, such as cross-validation, irregular intervals and random data distribution (to all available annotators) across the dataset, have also been adopted.

As it is evident, it is highly imbalanced, since the neutral class is assigned to the overwhelming majority of the cases, while the other two (and especially the positive class) are clearly underrepresented. If sentiment distribution is further analyzed on a per source basis, the most negative content (∼ 30%) appears on Twitter, a medium offering relatively anonymity (and thus, more "freedom") to its users. On the other hand, the least polarized opinions and at the same time the most "neutral" ones (more than 90%) appear on news articles. The latter are written by journalists who, most of the time, use a professional, unbiased language. Lastly, the most positive sentiment is expressed on Facebook comments (∼ 12%), which is three times more than the average. A similar analysis on a per domain basis is also interesting. By far, the most negative (and the least neutral or positive) feelings are expressed when politics are discussed, indicating that this is a highly polarized topic. The most neutral content, on the other hand, is again related to the banking sector, since most of the relevant content in the collected dataset originates from news articles, as it has been already argued. Finally, the transportation sector has received the most positive comments (around 10%).

Preprocessing

Prior to performing the sentiment prediction task, a number of data preprocessing steps are necessary. Initially, the textual part of each record is cleaned; that is, extra white space, non printable characters and other artifacts (e.g. HTML tags) are removed. Subsequently, the words that comprise the text are mapped to an embedding space, using fastText [START_REF] Bojanowski | Enriching word vectors with subword information[END_REF], a natural language processing methodology. In the end, the text of each document, is represented by the embeddings (vectors) of its words.

Among the non-printable characters that are extracted in the cleaning phase are emojis [START_REF] Novak | Sentiment of emojis[END_REF], a short of ideograms used in electronic communications to express feeling and emotions that are directly related to the sentimental state of the author of the document (e.g. smileys, sad or angry faces, etc). Since emojis do carry sentiment information, they are expected to positively contribute to the opinion mining task. A common methodology of including emojis in the prediction task would be to map them to a continuous vector space, usually consisting of two dimensions (sentiment score and neutrality) [START_REF] Novak | Sentiment of emojis[END_REF]. However, a different approach has been followed in this work; instead of using emoji embeddings, a vector designating the frequency of appearance of each emoji has been constructed for each record.

Another important characteristic that might be related to the sentiment value of a record is the number of its repetitions (retweets, shares, reposts, etc). The intuition behind this type of reasoning is that widely-spread content may carry significant emotional weight and therefore a correlation might exist between the number of times a text excerpt appears and its content. This characteristic follows a power law distribution in the dataset; the overwhelming majority of documents appear only once, while less than a 1, 000 records have been repeated more than 10 times. For this reason, in the model of Section 5.2, the logarithm of the number of repetitions is considered.

Experiments

The experiments that follow have been performed on the collected corpus presented above. In order to maintain temporal consistency, the dataset has been chronologically split into a training set (63.75% of the samples, earliest in time), a validation set (11.25% of the samples, subsequent in time) and a test set (25% of the samples, latest in time).

Feature Extraction

The predominant feature extraction activity involves the textual parts of each record in the collection. It is achieved by a stacked, two-layered BiLSTM network (Figure 2), which is considered to be among the state-of-the-art in capturing the spatial relationship between words and the order they appear in a text sequence [START_REF] Zhou | Text classification improved by integrating bidirectional LSTM with two-dimensional max pooling[END_REF]. The neural embeddings of the words are provided to the network in the order they appear in text, with a small amount of Gaussian noise (µ = 0, σ = 1) added to them, as a regularization effect that reduces overfitting. After extensive experimentation, the optimal number of units for each layer have been determined to be 150, with dropout layers applied in-between them (p = 0.3) [START_REF] Srivastava | Dropout: A simple way to prevent neural networks from overfitting[END_REF]. After the BiLSTM layers, an one-dimensional convolutional layer follows, with 64 filters and a window size of 5. Again, both of the aforementioned hyper-parameters have been determined after experimentation. Subsequently, a max-pooling layer of an equal window size downsamples the output of the convolutional layer. The textual feature extraction is finalized with an attention layer, whose addition counterbalances the decline in performance when dealing with long sentences. Lastly, the feature extraction procedure concludes with a Dropout layer (p = 0.5).

The other three features to be considered do not require such an extensive feature extraction procedure. Aspect is incorporated through aspect id, an onehot encoded variable (Section 3.1), while the presence of emojis is quantified as a frequency vector. Finally, the number of repetitions of each document is provided to the model via its logarithm (Section 4.1).

Model Selection

After experimenting with various techniques and architectures, the optimal model has been determined to be a fully-connected feed-forward artificial neural network, consisting of two hidden layers (Figure 3). The first hidden layer is comprised of 1024 neurons and the second of 128. Their activation function is the rectified linear unit [START_REF] Hahnloser | Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit[END_REF] (in contrast to the output layer, where softmax activation is used instead [START_REF] Goodfellow | Deep Learning[END_REF]). Network training has been based on the Adam optimization algorithm [START_REF] Kingma | Adam: A method for stochastic optimization[END_REF], with a learning rate of 10 -3 and hyperparameters β 1 , β 2 being fixed at 0.9 and 0.999, respectively. Finally, the fastText word embedding vectors used in the experiments have been pretrained on a corpus of more than 2, 000, 000 words. Fig. 3. Fully-connected model

Results

Figures 4-5 examine system performance regarding various aspects on Precision, Recall and their harmonic mean (F1-score); a set of popular information-retrieval metrics, widely used in sentiment analysis tasks [START_REF] Liu | Sentiment Analysis and Opinion Mining[END_REF]. The former is equal to the ratio of the correctly classified documents to a given class over the total number of classified documents to that class, while the latter is equal to the ratio of the correctly classified documents to a given class over the total number of documents that belong to that class. The values displayed in Figure 4 are averaged over all classes, while the results in Figure 5 are given for each class separately. Figure 4 summarizes system performance with respect to the different inputs. When only the textual part of each record is considered, the efficiency of the proposed approach is limited, an indication that in the environment described in Section 3, text alone is not a sufficient indicator for the prediction task. When aspect-related information is considered, Recall increases by more than 6% (followed by a smaller boost in Precision), meaning that the system can better discriminate in-between the classes. The addition of the logarithm of the number of repetitions marginally affects Recall, but further enhances Precision, with the F1-score in this case being slightly better than the previous one. Finally, when all four inputs are provided (text, aspect, logarithm of the number of repetitions, emojis), system throughput is further enhanced, with all three metrics being above (70%), adding more than 8% to the overall system performance.

Figure 5 displays the per-class performance of the examined metrics. Even though class labels are highly imbalanced (Table 3), the system achieves very good results for the negative class, a characteristic that is of significant importance, as the main concern of many businesses is to be able to timely identify and respond to unpleasant content. On the other hand, the predictions on the positive class are clearly below average and therefore more effort should be put in the direction of improving system efficacy for this particular case, as well.

Conclusions

In this work, a novel hybrid bi-directional LSTM/CNN feature extraction architecture has been presented, as part of a broader system that performs aspectbased sentiment analysis. The obtained results, on a corpus selected from Greeklanguage content from OSNs and other sources, are encouraging, especially on the negative class that is of particular interest to businesses. Nevertheless, the outlined architecture needs to be further fine-tuned and reasoned upon, as the system demonstrates sub-optimal performance in identifying positive sentiment. The proposed architecture may be extended in a number of ways. An obvious direction would be to consider additional textual characteristics that convey aspect and sentiment-based information. For instance, feature extraction from hashtags, which are quite popular on OSNs, is expected to further aid the desired task. Additionally, the number of repetitions could be leveraged by examining frequency patterns in-between the users that are either mentioned on or just redistribute content.

Finally, the quality of the already extracted features may be farther enhanced. For example, the application of dimensionality reduction techniques, such as principal component analysis, on the emoji frequency matrix can help determine which of the available emojis have the greatest impact on the sentiment analysis task.

Fig. 1 .

 1 Fig. 1. Overall system architecture

Fig. 2 .

 2 Fig. 2. Textual feature extraction procedure

Fig. 4 .

 4 Fig. 4. System performance w.r.t different inputs

Fig. 5 .

 5 Fig. 5. System Performance w.r.t different classes

Table 1 .

 1 Source medium distribution

	Source	Entries Percentage
	Tweets	160, 905	46.78%
	News articles	87, 750	25.51%
	Facebook posts	39, 591	11.51%
	Facebook comments 25, 250	7.34%
	Blog posts	14, 975	4.35%
	Instagram	10, 784	3.14%
	Other	4, 701	1, 37%
	Total	343, 956	100.00%

Table 2 .

 2 Domain distribution

	Domain Entries Percentage
	Telecom 178, 739	51.97%
	Tobacco	72, 822	21.17%
	Banks	36, 582	10.64%
	Politics	30, 677	8.92%
	Retail	11, 756	3.42%
	Transport 8, 079	2.35%
	Misc	5, 301	1.54%
	Total	343, 956	100.00%

Table 3 .

 3 Annotated sentiment distribution

	Sentiment class Percentage
	positive	4.03%
	neutral	78.53%
	negative	17.44%

Acknowledgements

This research has been cofinanced by the European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RE-SEARCH -CREATE -INNOVATE (project code: T1EDK-03470).


Paschalis Frangidis
email: frangidis@csd.auth.gr

Konstantinos Georgiou
email: georgiouka@csd.auth.gr

Stefanos Papadopoulos
email: stefanospi@csd.auth.gr

Sentiment Analysis on Movie Scripts and Reviews Utilizing sentiment scores in rating prediction

Keywords: Natural Language Processing, Sentiment Analysis, Machine Learning, Prediction

In recent years, many models for predicting movie ratings have been proposed, focusing on utilizing movie reviews combined with sentiment analysis tools. In this study, we offer a different approach based on the emotionally analyzed concatenation of movie script and their respective reviews. The rationale behind this model is that if the emotional experience described by the reviewer corresponds with or diverges from the emotions expressed in the movie script, then this correlation will be reflected in the particular rating of the movie. We collected a dataset consisting of 747 movie scripts and 78.000 reviews and recreated many conventional approaches for movie rating prediction, including Vector Semantics and Sentiment Analysis techniques ran with a variety of Machine Learning algorithms, in order to more accurately evaluate the performance of our model and the validity of our hypothesis. The results indicate that our proposed combination of features achieves a notable performance, similar to conventional approaches.

Introduction

Movie scripts are an interesting source of text, due to the diverse display of sentiments expressed in them. In principle, they are a storytelling device where the screenwriter is trying to convey something meaningful. The script's "emotional charge" is usually a tool to achieve the aforementioned goal or a byproduct of the process -and indeed a very important and powerful one. It is probably the most immediate point of resonance and communication with the audience. Movies usually contain scenes where emotions alter dynamically, between happiness and sadness, calmness and anger in order to aid the narrative progression, while some works are characterized by an overarching emotional 'weight', such as sadness in a tragedy-based film. In order to achieve this goal, the script needs to be written in a manner that captures the appropriate sentiments and allows the actors to portray it in their performances. It is very common nowadays, for the audience and critics alike to express their enjoyment of a movie or lack thereof, in the form of online reviews. In sites like "Metacritic" or "Rotten Tomatoes" reviews, analyses and criticisms are collected to create an average estimation of how popular and well-received a movie is. This information has already been exploited in many applications including movie recommendations and genre classification systems. However, most reviewers do not simply state their like or dislike of a movie and instead tend to focus on the different feelings that the movie evoked in them. It would be an interesting inquiry to analyze the intended emotion of a movie's script, compare it to the received emotional response of the reviewer and study whether the unison or dissonance between the two are correlated or not. The primary goal of this study is to accumulate movie scripts and their respective reviews in order to examine the validity of the aforementioned statement and examine whether the relationship between the intended emotional weight of the movie and the received emotion of the reviewer can help in accurately predicting movie ratings.

Background and Related Work

Sentiment analysis, as a field of study, has met a significant increase in its applicability in various sectors, as it can easily become cross disciplinary and facilitate different processes [START_REF] Mesnil | Ensemble of Generative and Discriminative Techniques for Sentiment Analysis of Movie Reviews[END_REF][START_REF] Pang | Opinion Mining and Sentiment Analysis[END_REF][START_REF] Medhat | Sentiment analysis algorithms and applications: A survey[END_REF]. Its simplicity and straightforward approach have established it as a respected factor of text analysis. Indicative sectors where sentiment analysis is implemented are business systems, marketing campaigns and recommender systems [START_REF] Chakraborty | Comparative Sentiment Analysis on a Set of Movie Reviews Using Deep Learning Approach[END_REF].

In recent years, sentiment analysis in conjunction with NLP and ML techniques have been used in an array of different applications concerning movie scripts and their reviews. They have been used in order to identify patterns in movie structures [START_REF] Lee | Analyzing Movie Scripts as Unstructured Text[END_REF] and learn to predict the following emotional state based on the previous [START_REF] Sahu | Sentiment analysis of movie reviews: A study on feature selection & classification algorithms[END_REF], showing respectively that 'successful' movies follow specific narrative progressions and have a certain "flow" and consistency in the way that emotional states unfold. Additionally, it has been observed that existing binary ("positive/negative") sentiment analysis techniques have a 'positivity bias', favoring the learning of positive emotions over negative ones, hence "underestimating" the latter's existence. This can create a significant discrepancy in accuracy of around 10 to 30%. It is shown that taking into consideration meta-features (capital letters, punctuation and parts of speech) helps mitigate this problem [START_REF] Kim | Detecting Multiclass Emotions from Labeled Movie Scripts[END_REF].

Other approaches have tried to classify reviews based on semantically similar words [START_REF] Chakraborty | Comparative Sentiment Analysis on a Set of Movie Reviews Using Deep Learning Approach[END_REF] in order to detect communities of reviewers via clustering. Some interesting alternatives propose the identification of the driving aspects of a movie, them being mainly associated with the screenplay, the acting and the plot or some more particular characteristics (e.g. music, effects) [START_REF] Parkhe | Sentiment analysis of movie reviews: finding most important movie aspects using driving factors[END_REF], and how they are reflected on the reviews in sentiments. Results indicate that the acting and the plot are usually the most important factors that influence a review. However, variations in these studies which examine the same aspects in different movie genres may alter the driving factors in respective reviews. Typically, the methodology implemented to perform sentiment analysis, directly involves classic text mining solutions such as N-Gram extraction or Part-of-Speech tagging in combination with Naïve Bayes (NB) or Recurrent Neural Networks (RNN) [START_REF] Sureja | Using Sentimental Analysis Approach Review on Classification of Movie Script[END_REF]. However, different techniques, such as the use of a Gini Index or Support Vector Machine (SVM) approaches [START_REF] Manek | Aspect term extraction for sentiment analysis in large movie reviews using Gini Index feature selection method and SVM classifier[END_REF][START_REF]Rotten Tomatoes Movie Database[END_REF] have increased the accuracy of results. The Skip Gram and Continuous Bag of Words (CBOW) models [START_REF] Chakraborty | Comparative Sentiment Analysis on a Set of Movie Reviews Using Deep Learning Approach[END_REF] have also shown promising results.

A recent research venture [START_REF] Giatsoglou | Sentiment analysis leveraging emotions and word embeddings[END_REF] has tried a mixture of techniques to extract the emotion out of reviews. Using a combination of emotion lexicon and word embeddings in order to extract reviews' sentiment, they acquire a satisfactory level of prediction to the reviewers' binary score of the movie. Moreover, it provides solutions for both English and Greek datasets. This particular approach is going to be extended in the current research. Regarding the classification of movie scripts based on NLP, there have been attempts in using subtitle files of different movies to ultimately predict their genre [START_REF] Mesnil | Ensemble of Generative and Discriminative Techniques for Sentiment Analysis of Movie Reviews[END_REF].

The premise is that via sentiment analysis of the subtitles' context, sentiment can be extracted and thus, the genre of the movie can be identified. Initial findings show that these techniques yield better results when applied to action, romance or horror movies. However, further analysis could strengthen the prediction margin.

The arising importance of sentiment analysis in movie and review evaluation has also prompted well known and respected online competition networks like Kaggle [START_REF]Rotten Tomatoes Movie Database[END_REF] to organize initiatives in order to suggest innovative solutions to the problem. It is evident, though, that in order to produce a well-documented solution, features have to be carefully selected, taking into consideration the variance of terms, the handling of negation and the treatment of opinion words [START_REF] Manek | Aspect term extraction for sentiment analysis in large movie reviews using Gini Index feature selection method and SVM classifier[END_REF].

Our paper contributes to the current scientific framework by exploiting data both from movie scripts and their corresponding reviews and by applying two specialized lexicons in order to conduct sentiment and emotion analysis. We believe our research to be of notable importance as it can indicate a new approach to the problem which treats the review text and the movie script as coexisting entities and merges them for optimal results.

Data Collection and Preprocessing

In order to gather data for movie scripts and movie reviews, we used simple scrapers in Python to crawl specific web pages. For the movie scripts, we used the IMSDB website, which contains more than 1100 movie scripts and drafts. Our reviews were gathered from the Rotten Tomatoes website, a well-known source for reviewing films, also by using a web scraper. The scripts gathered were in .txt format and the reviews were saved in a CSV file. Before continuing to the preprocessing phase, we noticed a significant imbalance in our review dataset which contained 55.886 fresh/positive movies and 22.193 rotten/negative. This imbalance would create inaccurate predictions in our model and in order to correct it, we performed undersampling, keeping all of the "rotten" and 25.000 of the "fresh" reviews. "Fresh/Rotten" values were transformed into 0 and 1 and were used as the model's target variable.

The scraper utilized for obtaining the movie scripts has a quite simple and easy to understand structure. It uses the Beautiful Soup package and redirects to the IMSDB website, locating all the script titles, categorized in alphabetical order. The scraper's first operation is to store all the <a> tags, corresponding to movie titles, in a list. Afterwards, it iterates the list of titles and redirects to the corresponding URLs, where it loads scripts in .txt files and creates a directory to store them. The scraper keeps only data present in <body> tags and opts to ignore tags like <head> or <footer>.

To obtain the corresponding movie reviews, we modified a Beautiful Soup based scraper which collected review texts and stored them to a shared CSV file. Our data were limited due to the reason that Rotten Tomatoes only contains an excerpt of the original review. During the collection phase, we filtered reviews which contained no text, as we deemed them unsuitable for our model.

In the preprocessing phase, we applied sentence splitting and word tokenization. Punctuation marks and stop-words were removed but only after experimenting and keeping track of the results with their inclusion. We then created a punctuation-removal list which consisted of all the punctuation marks we aimed to remove. At first, only exclamation marks were excluded from the punctuation list and the words "not" and "but' from the stopword-removal list in order to study their effects on the VADER sentiment analysis, as discussed in the next section. Their effect on the predictive accuracy was considered negligible, hence all stop words and punctuation marks were eventually removed. Finally, we applied part-of-speech tagging and lemmatization because we noticed that the lexicon-based sentiment analysis tools we selected lacked many derivatives and would lose out on a significant segment of our dataset.

Methodology

Our proposed model is based on calculating the emotional weight of a movie's script and combining it with the emotion expressed by the reviewer. For Sentiment and Emotion Analysis, we considered many different tools but ended up selecting VADER and NRC. VADER (or Valence Aware Dictionary and sEntiment Reasoner) is a binary Sentiment Analysis tool using a dictionary approach, containing 7.518 uni-grams including punctuation, slang words, initialisms, acronyms and emoticons. VADER receives a sentence as input and returns 4 values, negative, neutral, positive and compound which is the 'normalized weighted composite score'. Each output is ranging from -1,5 to +1,5, from 'Very Negative' to 'Very Positive', but we normalized them into a range of {0 to 1}. VADER is widely used [START_REF] Park | Sentiment analysis of Twitter corpus related to artificial intelligence assistants[END_REF][START_REF] Newman | Sentiment Analysis of Student Evaluations of Teaching[END_REF] and preferred as a sentiment analysis tool because of its advanced heuristics. It includes 5 built-in and pre-trained heuristics taking into consideration punctuation marks (especially exclamation points), capitalization, degree modifiers (boosters and dampeners) Shift Polarity (with words like "but") and Negation Handling using tri-grams.

NRC, on the other hand, is an emotion analysis tool, categorizing a sentence into eight emotions (anger, fear, anticipation, trust, surprise, sadness, joy, disgust) and two sentiments (positive, negative). Regarding the selected emotions in the NRC lexicon, there is a consensus among Psychology Researchers about Joy, Sadness, Anger, Disgust, Surprise and Fear being categorized as 'Basic Human emotions' showing their importance and universal nature [START_REF] Kowalska | Basic Emotions[END_REF]. In addition to these six emotions, the NRC contains words about Trust and Anticipation and although they may not be considered as 'basic emotions' by everyone, we believe them to be important especially for analyzing texts of movie scripts and their reviews. This lexicon contains more than 14000 words, with each one being scored on every emotion. We had to calculate the average of these ten emotion scores for every review, after having summed the scores for every word. We hypothesize that NRC is better suited for the model we are trying to build due to the complexity of movie scripts but both sentiment analysis tools were tested empirically.

Fig. 1. Applied workflow

Our model requires the concatenation of the emotionally analyzed of both the movie scripts and their reviews using the NRC lexicon (Fig. 1.). In order to evaluate our model more reliably, we reproduced many different conventional methods for movie rating predictions. As features we used Vector Semantics like CountVectorizer and TF-IDF in combination with NRC and VADER. Indicatively, the experimental combinations that were tested on the movie reviews alone were: The dataset was split into train and test data, by 75% and 25% respectively. Finally, regarding the selection of machine learning algorithms we experimented with the following algorithms: Multinomial Naive Bayes (MNB), Logistic Regression (LR), SVM and Multilayer Perceptron (MLP). All algorithms were able to return satisfactory results but MNB was selected because it consistently resulted in higher predictive accuracy (Table 1) and was significantly faster computationally. We selected to test the four machine learning models against two feature combinations, CV as a representative of Vector Semantic approaches and CV in combination with NRC to add information about the emotions into the equation.

MNB

Results

The evaluation of the aforementioned experiments was conducted with the use of Accuracy, Precision, Recall and F1 as the selected metrics. The bar plot below (Fig. 2), presents an indicative selection of experiments, from the total that were performed, which we considered to be the most important. Based on the visualized results, it seems that the CountVectorizer method produces slightly better results than TF-IDF method, possibly because of better document representation. The VADER lexicon showed the worst performance when used on its own. Additionally, it appears that its heuristics, despite increasing the intensity of the sentiment, did not play a crucial role in improving the predictive accuracy. VADER's low performance was expected, as we initially hypothesized that binary sentiment analysis (positive/negative) was too simplistic to capture the complexity of a movie's script. We believe that the problem lies in the interpretive ambiguity of binary sentiment approaches. A negative sentiment score for a review can have two very different meanings. It may show legitimate dislike for the movie or it may express the reviewer's experience of sadness or horror which can technically be considered as "negative" emotions but are expected in an effective and wellmade drama or horror movie respectively. Furthermore, confirming our hypothesis, the NRC lexicon greatly outperforms VADER by 10%, proving the importance of taking into consideration a multitude of emotions. An unexpected finding is that the combination of Vector Semantics approaches with sentiment lexicons yields significantly different results based on which lexicon is applied. The VADER lexicon slightly enhances the performance of the CountVectorizer -if only by 0.1% -while the NRC lexicon reduces the performance. This can possibly be attributed to the relative increase in complexity of the model when combined with the multiple emotions of the NRC lexicon, which led to constrained results. Finally, our proposed model, which took into consideration both the review and the script emotions without any other NLP technique, managed to reach impressive precision percentages, only 0.8% lower than the best performing model but didn't quite reach the same level of accuracy. While not being the best performing model, it indicates the potential and validity of our initial hypothesis which dictated that the relationship between the expressed emotion of the movie and the received emotion by the reviewer can be a potent predictor for movie's rating. Generally, Vector Semantics approaches (TF-IDF and CountVectorizer) performed much better than simply using sentiment and emotional lexicons. They seem better at identifying the importance of each word inside the text which helps the machine learning model to comprehend its structure and any possible hidden patterns. Sentiment analysis tools are already being used in a variety of cases, but they are still in a transitional stage. NRC, especially, doesn't take at all into consideration the context of a word -it simply returns its pre-classified score -which we believe to be the most important reason for its lacking performance. Another factor that probably contributed to our model's restrained performance was the absence of the totality of each review. We only had a small excerpt from the whole review provided by Rotten Tomatoes. By analyzing the review in its totality it's possible that our model's predictive accuracy would have improved.

Conclusions -Future Work

This paper explored the effect of incorporating emotion and sentiment analysis in predicting movie ratings and more specifically, the combinations of the movie script's intended emotion and the emotional response of the reviewer. Data were collected from relevant sources and contained a fair amount of scripts and reviews. We tried several machine learning models and experimented with the usage of sentiment and emotion analysis with two well-known lexicons, one intended for sentiment analysis and one for emotion analysis. We examined their performance on their own and in combination with Vector Semantics tools. The performance of our proposed model was actually quite impressive, almost reaching the best performing model in precision but we believe that the results were limited by the lack of negation handling and the fact that we used only excerpts of the reviews, provided by Rotten Tomatoes, and not the whole text. We believe that the accuracy of the model would improve if we were able to obtain the whole body of the reviews and thus have a larger corpus for analysis. However, the results may still end up being unsatisfying, due to the absence of negation handling and polarity shift which should also be tested. Of course, such enhancements would require different preprocessing of the data by keeping appropriate stop words or applying different representation methods.

Regarding suggestions for improving similar models in the future, we thought it would be useful to incorporate character and contextual analysis. Although sentiment can indeed be extracted from simple words, there should be additional weight to some character names. For example, the names of some famous villains can easily be associated with a specific sentiment, like fear, and thus should be considered non neutral words when conducting an analysis of sentiment. This can lead to better results in many cases and better characterization of the script's context as a whole.

Another possible expansion of the model would be the implementation of different document representation methods, such as Word2Vec embeddings or the use of N-Grams.

It would be interesting to explore how different methods affect the quality of results and whether the combinations of lexicons and approaches would have similar outputs. Finally, we considered that a possible practical use of our model would be to expand its use not only in reviews but other forms of criticism like YouTube Comments or Tweets and apply the same methodology to give an early estimation of a movie's box office success or failure. It is obvious that such predictions will only be vague, as a movie's gross depends on a variety of factors (marketing, production, cast, merchandise etc.) but this model can certainly serve as a supplementary tool for further assurance of success.

Fig. 2 .

 2 Fig. 2. Evaluation scores of the proposed model and the baseline methods. The proposed model is referred as "Script + Review (NRC only)".

  Our proposed model was tested in conjunction with more conventional approaches by running multiple experiments with different combinations of features like Vector Semantics, typical Sentiment Analysis and using different Machine Learning algorithms.

The remaining chapters are structured as follows: in Chapter 2, we present some related work and pinpoint our contribution to the topic. In Chapter 3, we analyze the dataset and present our key features, and in Chapter 4 the methodology applied is explained. In Chapter 5, the basic results of our study are presented and discussed, while in Chapter 6 we provide our main conclusions, accompanied by some interesting future work suggestions.

Table 1 .

 1 F1-scores of the four compared ML models.

	LR	MLP	SVM

ACKNOWLEDGEMENTS

This paper was directly based on an assignment we completed during our "Text Mining and Natural Language Processing" Course in the Data and Web Science MSc program of the School of Informatics of the Aristotle University of Thessaloniki. We would like to personally thank Dr. Grigorios Tsoumakas, our supervising professor, who provided direction, reviewed the project and encouraged us to submit it as a conference paper.


Qian Feng

Yiming Song
email: ymsong@nwu.edu.cn

Yixuan Ma

Yan Deng

Pengyue Xu

Kangjia Sheng

Yongmin Zhang

Jianli Li

Shaoping Wu
email: wushaoping@nwu.edu.cn

Molecular engineering of benzenesulfonyl analogs for visual hydrogen polysulfide fluorescent probes based on Nile red skeleton

Keywords: Nile red, Fluorescent probe, H 2 S n, Cell imaging, Zebrafish imaging

Hydrogen polysulfide (H 2 S n , n>1) has a valuable function in various aspects of biological regulation. Therefore, it is of great significance to realize the visual monitoring of H 2 S n levels in vivo. Herein, a series of fluorescent probes NR-BS were constructed by changing types and positions of substituents on the benzene ring of benzenesulfonyl. Among them, probe NR-BS4 was optimized due to its wide linear range (0~350 μM) and little interference from biothiols. In addition, NR-BS4 has a broad pH tolerance range (pH = 4~10) and high sensitivity (0.140 μM).

In addition, the PET mechanism of probe NR-BS4 and H 2 S n was demonstrated by DFT calculations and LC-MS. The intracellular imaging studies indicate that NR-BS4 can be successfully devoted to monitor the levels of exogenous and endogenous H 2 S n in vivo.

Introduction

Reactive sulfur species (RSS) have redox activity under physiological conditions, mainly including reduced glutathione (GSH), sulfur dioxide (SO 2 ), cysteine (Cys), sulfite/bisulfite (SO 3 2-/HSO 3 -), homocysteine (Hcy), hydrogen polysulfide (H 2 S n , n>1) and hydrogen sulfide (H 2 S). RSS give full play to superiority in adjusting redox activity [START_REF] Kasamatsu | Regulation of redox signaling by reactive sulfur species[END_REF] and maintaining mitochondrial health [START_REF] Wang | Rhodanese Rdl2 produces reactive sulfur species to protect mitochondria from reactive oxygen species, Free Radical Bio[END_REF]. However, RSS disorders can also induce a variety of diseases such as arteriosclerosis [START_REF] Ishii | Reactive sulfur species inhibit the migration of PDGF-treated vascular smooth muscle cells by blocking the reactive oxygen species-regulated Akt signaling pathway[END_REF], diabetes [START_REF] Takahashi | Reactive sulfur species regulate tRNA methylthiolation and contribute to insulin secretion[END_REF],

neurodegenerative diseases [START_REF] Sun | Role of hydrogen sulfide and polysulfides in neurological diseases: focus on protein S-persulfidation[END_REF], aging [START_REF] Yamanaka | Reactive sulfur species are involved in cigarette smoke-induced cellular senescence[END_REF] and schizophrenia [START_REF] Cao | Protective smell of hydrogen sulfide and polysulfide in cisplatin-induced nephrotoxicity[END_REF]. Among these RSS molecules, H 2 S is the third gas signal molecule with biological activity after NO and CO, which has been extensively studied in recent years [START_REF] Zhang | The regulatory role of MiR-203 in oxidative stress induced cell injury through the CBS/H 2 S pathway[END_REF][START_REF] Liu | Potential target of H 2 S against atherosclerosis[END_REF][START_REF] Liu | Two simple but effective turn-on benzothiazole-based fluorescent probes for detecting hydrogen sulfide in real water samples and HeLa cells[END_REF][START_REF] Zhong | A near-infrared fluorescent probe for H 2 S based on tandem reaction to construct iminocoumarin-benzothiazole and its application in food, water, living cells[END_REF]. With further research, H 2 S n (n>1) has been found to be more effective than H 2 S in adjusting the activities of ion channels, transcription factors, protein kinases and tumor suppressors [START_REF] Kimura | Signalling by hydrogen sulfide and polysulfides via protein S-sulfuration[END_REF][START_REF] Kimura | Signaling molecules: hydrogen sulfide (H 2 S) and polysulfides (H 2 S n )[END_REF].

Obviously, many of the biological functions previously thought to be attributed to H 2 S may actually be performed by H 2 S n [START_REF] Yadav | Biosynthesis and reactivity of cysteine persulfides in signaling[END_REF]. Moreover, H 2 S n has many biological functions such as anti-oxidation ion channel activation and nephrotoxicity inhibition [START_REF] Olson | Effects of inhibiting antioxidant pathways on cellular hydrogen sulfide and polysulfide metabolism, Free Radical Bio[END_REF][START_REF] Yang | Modulation of K + channel N-type inactivation by sulfhydration through hydrogen sulfide and polysulfides[END_REF][START_REF] Sun | Polysulfide and hydrogen sulfide ameliorate cisplatin-induced nephrotoxicity and renal inflammation through persulfidating STAT3 and IKK beta[END_REF]. In order to have a deeper understanding of its formation, molecular reactions and regulatory mechanisms, it is necessary to exploit accurate and highly specific tools to monitor H 2 S n level in vivo.

Although the traditional monitoring technologies mainly involving ultraviolet-visible spectroscopy [START_REF] Debiemme-Chouvy | First evidence of the oxidation and regeneration of polysulfides at a GaAs electrode, under anodic conditions. A study by in situ UV-visible spectroelectrochemistry[END_REF], mass spectrometry [START_REF] Gun | Electrospray ionization mass spectrometric analysis of aqueous polysulfide solutions[END_REF] and high-performance liquid chromatography [START_REF] Koike | Analysis of endogenous H 2 S and H 2 S n in mouse brain by high-performance liquid chromatography with fluorescence and tandem mass spectrometric detection, Free Radical Bio[END_REF] could detect H 2 S n in trace amounts, they usually require complex sample pretreatment, and the most critical problem is that they cannot be monitored in real time in organisms. At present, fluorescence analysis technology has become the most feasible method for detecting H 2 S n due to its highly temporal and spatial resolution, high sensitivity, non-invasive and fast real-time imaging [START_REF] Zhao | Biothiol-triggered H 2 S release from a near-infrared fluorescent H 2 S donor promotes cutaneous wound healing[END_REF][START_REF] Li | A water-soluble and incubate-free fluorescent environment-sensitive probe for ultrafast visualization of protein thiols within living cells[END_REF].

The first H 2 S n -specific probe, DSP, was reported by Xian's group in 2014 [START_REF] Liu | Rational design and bioimaging applications of highly selective fluorescence probes for hydrogen polysulfides[END_REF], and a great quantity of H 2 S n fluorescent sensors have been ensued, primarily including 2-fluoro-5-nitrobenzoic ester [START_REF] Sun | A FRET-based ratiometric fluorescent probe for highly selective detection of hydrogen polysulfides based on a coumarin-rhodol derivative[END_REF], aziridine [START_REF] Chen | A specific nucleophilic ring-opening reaction of aziridines as a unique platform for the construction of hydrogen polysulfides sensors[END_REF], nitro [START_REF] Chung | Two-photon imaging of hydrogen polysulfides in living cells and hippocampal tissues[END_REF], cinnamate ester [START_REF] Hou | Development of fluorescent probes for hydrogen polysulfides by using cinnamate ester as the recognition unit[END_REF],

acrylate ester [START_REF] Guo | Molecular engineering of alpha-substituted acrylate ester template for efficient fluorescence probe of hydrogen polysulfides[END_REF] and 2-benzoylsulfanylbenzoic acid [START_REF] Zhao | Biothiol-triggered H 2 S release from a near-infrared fluorescent H 2 S donor promotes cutaneous wound healing[END_REF] as recognition groups (Fig. 1). However, these probes have certain limitations, such as poor selectivity, long response time, and narrow linear range. Therefore, new recognition units need to be proposed for the development of H 2 S n probes. Based on the fact that 2, 4-dinitrobenzenesulfonyl is a classical recognition site for biothiols and the strong nucleophilicity of H 2 S n compared to biothiols, we envisioned an engineering to explore between different benzenesulfonyl analogs and efficient H 2 S n fluorescent probes (Fig. 2). To this end, a series of fluorescent probes NR-BS based on Nile red skeleton were designed and synthesized. Among them, NR-BS4 with 3-hydroxy NR as the skeleton and 2-nitrobenzenesulfonyl as the reaction site has the advantages of wide linear range (0~350 μM) and high selectivity.

Most importantly, probe NR-BS4 has low toxicity and superior ability to detect H 2 S n level in cells and zebrafish.

Experimental section

Synthesis of probe NR-BS4

Compound 2b (33.4 mg, 0.100 mmol, 1.00 equiv.) was dissolved in DCM and cooled to 0℃. Et 3 N (55.0 μL, 0.210 mmol, 2.10 equiv.), 2-nitrobenzene sulfonyl chloride (44.3 mg, 0.200 mmol, 2.00 equiv.) were dissolved in the main reaction flask and allowed to react for 2 h at room temperature. After monitoring by TLC to ensure that compound 2b was exhausted, the reaction mixture was extracted with DCM and water, the DCM layer was dried and evaporated. Then, the residue was purified by flash column chromatography (DCM:MeOH = 200:1) to give NR-BS4 (89.2% yield). with sulfur in an aqueous solution at 70 ℃ for 1.5 h. In general, the reaction of probes NR-BS with Na 2 S 2 was performed in PBS (10.0 mM, pH 7.40, 20% DMSO, 100 μM CTAB). Then, the reaction solution was measured for its fluorescence intensity and scanned at 590 nm.

Cell imaging

For exogenous imaging, HepG2 cells with NR-BS4 (5.00 μM) were incubated at 37 ℃ for 30 min, then bred with changed volume of Na 2 S 2 (0 μM, 25 μM, 100 μM and 180 μM) for 15 min. For endogenous imaging, HepG2 cells were split into two plates. One disc was stimulated with lipopolysaccharide (LPS, 1.00 μg•mL -1 ) for 20 h, and then conducted to incubate with NR-BS4 for 30 min. The other disc was proceeded to incubate with DL-propargylglycine (PAG, 100 μM) and NR-BS4 for 30 min, and then LPS was incubate for 20 h. Next, an excitation wavelength of 552 nm was selected to collect the emission light within the limits of 560 to 700 nm, the images were obtained under a confocal laser microscope.

Zebrafish imaging

Three-day-old zebrafish were selected for imaging and divided into two groups of six fish each. Two groups of fish were loaded with NR-BS4 and cultured at 28 ℃ for 30 min. One group was performed to incubate with 350 μM Na 2 S 2 for 15 min, while the other group was added by E3 medium as a control. Imaging was performed under the same conditions as for cell imaging.

Results and discussion

Design of probes NR-BS

Nile red dye (NR) has been recognized as a potential candidate with a wide range of applications due to excellent characteristics, including controllable emission and high photostability. Above all, it has a long fluorescence emission wavelength (λ em > 650 nm), which can effectively reduce the background fluorescence in complex samples, and improve the detection sensitivity and accuracy. As we know, 2, 4-dinitrobenzenesulfonyl is a powerful recognition group for biothiols via nucleophilic substitution [START_REF] Zhang | A turn-on fluorescent probe based on 2, 4-dinitrosulfonyl functional group and its application for bioimaging[END_REF]. Inspired by this recognition group (Fig. 2a) and considering the strong nucleophilic properties of H 2 S n , we speculated that this kind of benzenesulfonyl analog should also be able to capture H 2 S n , thereby releasing the masked fluorophore and restoring its fluorescence. We assumed that the specific detection of H 2 S n could be achieved by changing types and positions of the substituents on the benzene ring of benzenesulfonyl to affect biothiols. Furthermore, it has been found that the different hydroxyl positions of NR will lead to differences in its reactivity and fluorescence properties. To verify the assumptions, we selected 2-or 3-hydroxy NR as the fluorophore scaffold, modified with 14 commercial benzenesulfonyl derivatives, and constructed a series of fluorescent probes NR-BS.

We presume that the electron-withdrawing groups (-NO 2 , -CF 3 , -CN, -Cl and -OCH 3 ) could suppress the fluorescence of NR-OH by way of PET, while the electron-donating group (-CH 3 ) cannot produce PET process, as a control. When the strongly nucleophilic H 2 S n is added, the sulfonate ester is cleaved, releasing NR-OH with high fluorescence intensity (Fig. 2b).

The synthesis process is illustrated in Scheme 1. Specific synthesis steps and detailed spectra of structural characterization are shown in the Supporting Information.

Fluorescent spectral properties and responses for H 2 S n

The source of H 2 S 2 , the main representative compound of H 2 S n , is usually obtained by preparing a fresh Na 2 S 2 solution [START_REF] Gao | A near-infrared fluorescent probe for the detection of hydrogen polysulfides biosynthetic pathways in living cells and in vivo[END_REF]. Within NR-BS hand, we firstly tested the spectral properties and responses for Na 2 S 2 under imitated physiological circumstances (10.0 mM PBS, pH 7.40, encompassing 20% DMSO and 100 μM CTAB), in which benzenesulfonyl analogs substituted with -NO 2 and -CF 3 produced red fluorescence, named NR-BS1, NR-BS2, NR-BS3, NR-BS4 and NR-BS5, respectively. The response time, linear range, and fluorescence enhancement multiples, were further studied for these five compounds. The fluorescence intensity of NR-BS1, NR-BS2, NR-BS3, NR-BS4 and NR-BS5 showed linear relationship within the scope of 0~300 μM, 0~5.00 μM, 0~200 μM, 0~350 μM, and 0~10.0 μM, respectively (Fig. 3). The selectivity of the five probes for the determination of Na 2 S 2 was further compared, and their responses to the interfering molecular biothiols were investigated, and the data are shown in Table S4. Considering that NR-BS4 has the advantages of wide linear range, high response fold, and no interference from biological thiols compared to the other four compounds, it was selected as a representative probe for follow-up examination. When NR-BS4 existed alone, it emitted powerless fluorescence. As Na 2 S 2 was gradually added, the fluorescence intensity at 666 nm also gradually enhanced (Fig. 4a), and the linear range was 0~350 μM (Fig. 4b, R 2 = 0.9984). According to the formula listed in Table S2, LOD was figured up 0.14 μM. These data confirm that NR-BS4 can achieve quantitative detection of Na 2 S 2 in vitro with good selectivity, excellent sensitivity, and wider linear range compared to the published probes listed in Table S1.

Kinetic study of probe NR-BS4 on H 2 S n

For the purpose of successfully working for biofluorescence imaging, NR-BS4 must be activated in the proper physiologic pH range. Therefore, we tested the sensitivity of NR-BS4 to Na 2 S 2 at different pH values (2~13) using fluorescence spectrometry (Fig. 4c). Additionally, the time-driven phenomenon of NR-BS4 was observed. The fluorescence intensity of NR-BS4 gradually heightened and reached a plateau at 666 nm for 10 min, and then remained basically unchanged (Fig. 4d), which proved that NR-BS4 could be used as a tool to achieve monitoring in vivo.

Selectivity and competition of NR-BS4 to H 2 S n

To determine the selectivity of NR-BS4 toward Na 2 S 2 , a series of other fluorescence experiments with interfering small molecules were performed. As shown in Fig. 5a, the probe NR-BS4 had only a fluorescence-on response to Na 2 S 2 . While Na 2 S and NaHS caused weak fluorescence enhancement, but it could be ignored.

These data demonstrate that NR-BS4 exhibits good selectivity for Na 2 S 2 under physiological conditions. Then the competition of NR-BS4 in the complex system was measured. Even when other interfering substances were added, the fluorescence signal of NR-BS4 for Na 2 S 2 still existed (Fig. 5b). These results conclude that NR-BS4 could selectively respond to Na 2 S 2 against a variety of potential interfering species in the biological media.

DFT calculations

In order to fully understand the fluorescence change mechanism of probe NR-BS4 and compound 2b, DFT calculations were performed using the B3LYP/6-311+G(d) level of the Gaussian 09 program. The solvent effects were considered in DMF using the polarizable continuum model (PCM). As shown in Fig. 6, the LUMO energy (-3.68 eV) of 2-nitrobenzenesulfonyl chloride (DNS) is between the HOMO energy (-5.18 eV) and LUMO energy (-3.13 eV) of compound 2b, which proves that PET can happen.

Fluorescence imaging in living cells

The cytotoxicity of NR-BS4 was quantified by the MTT assay prior to biological applications. Fig. S1 indicates that NR-BS4 has cytotoxicity, and a concentration of 5.00 μM was selected for subsequent cell imaging.

First, the imaging ability of NR-BS4 in cells was explored with exogenous Na 2 S 2 .

The weak red fluorescence appeared after probe-loaded HepG2 cells were incubated at 30 min, confirming the good cell permeability of NR-BS4 (Fig. 8a-c). When different concentrations of Na 2 S 2 were added and incubated for 15 min, followed by 30 min incubation with probe NR-BS4, strong red fluorescence was observed (Fig.

8d-i).

NR-BS4 NR-BS4+25 μM S

Conclusion

In summary, inspired by the recognition group 2,4-dinitrobenzenesulfonyl of biothiols, we explored the effects of the types and positions of substituent on the benzene ring of benzenesulfonyl on the responses of biothiols and H 2 S n . Furthermore, we think that the different positions of the hydroxyl groups of NR could lead to differences in its reactivity and fluorescence properties. Based on this, we selected 2or 3-hydroxy NR as the fluorophore scaffold, modified with 14 commercial benzenesulfonyl derivatives, and constructed a series of fluorescent probes NR-BS.

After the evaluation of fluorescence properties, the optimal probe NR-BS4 was found to have a rapid response (< 10 min), high sensitivity (0.140 μM), wide linear range (0~350 μM), large pH tolerance range (pH = 4~10) and good biocompatibility, so as to detect H 2 S n levels in the organism. We believe that our work could provide a new identification group that will be further used to explore the biological and pathological functions of H 2 S n .

Fig. 1 .

 1 Fig. 1. Commonly used recognition sites for H 2 S n probes.

Fig. 2 .

 2 Fig. 2. Design of novel probes NR-BS.

Scheme 1 .

 1 Scheme 1. Synthesis of probes NR-BS. (a) NaNO 2 , HCl, H 2 O, 3.5 h, 77.1%; (b) Dihydroxynaphthalene, DMF, reflux, 6 h, 31.3%; (c) Et 3 N, DCM, r.t., 2 h.

2. 2 .

 2 Titration experiments of probes NR-BS Before the fluorescence experiment, probes NR-BS were prepared as a solution (1.00 mM) by DMF. In addition, Na 2 S 2 solution was prepared by reacting Na 2 S•9H 2 O

Fig. 3 .

 3 Fig. 3. Linear relationship between probes and Na 2 S 2 concentration at 590 nm. (a) 10.0 μM of NR-BS1 with Na 2 S 2 (0~300 μM), (b) 10.0 μM of NR-BS2 with Na 2 S 2 (0~5.00 μM), (c) 10.0 μM of NR-BS3 with Na 2 S 2 (0~200 μM), (d) 10.0 μM of NR-BS4 with Na 2 S 2 (0~350 μM), (e) 10.0 μM of NR-BS5 with Na 2 S 2 (0~10.0 μM).

Fig. 4 .

 4 Fig. 4. Spectral properties of probe NR-BS4. (a) Fluorescence titration spectrum of probe NR-BS4 (10.0 μM) with different concentrations of Na 2 S 2 (0~500 µM) in PBS (10.0 mM, pH 7.40, 20% DMSO) at room temperature. (b) Linear relationship between probe NR-BS4 (10.0 μM) at 590 nm and Na 2 S 2 concentration (0~350 µM). (c) pH-dependent fluorescence changes of NR-BS4 (10.0 μM) in the absence and present of Na 2 S 2 (350 μM). (d) Time-dependent fluorescence changes of NR-BS4 (10.0 μM) with Na 2 S 2 (350 μM) in phosphate buffer. λ ex = 590 nm, λ em = 666 nm, slits: 5.00 nm / 10.0 nm, volt: 850 V.

Fig. 5 .

 5 Fig. 5. (a) Fluorescence spectrum of probe NR-BS4 (10.0 μM) upon addition 350 μM 16 kinds of analytes in PBS buffer solution. (b) Fluorescence spectrum of probe NR-BS4 (10.0 μM) under simultaneously presence of 350 μM Na 2 S 2 and other analytes in PBS buffer solution (1. Na 2 SO 3 , 2. NaHSO 3 , 3. Na 2 SO 4 , 4. NaHSO 4 , 5. Na 2 S 2 O 3 , 6. Na 2 S 2 O 5 , 7. NaClO, 8. H 2 O 2 , 9. GSH, 10. Cys, 11. Hcy, 12. Met, 13. Na 2 S, 14. NaHS, 15. Blank, 16. Na 2 S 2 ).

Fig. 6 .

 6 Fig. 6. Structural optimization of DNB, compound 2b, and NR-BS4 by DFT.

Fig. 7 .

 7 Fig. 7. Reaction mechanism of probe NR-BS4 with Na 2 S 2 。(a) LC-MS chromatograms: eluent, CH 3 CN/H 2 O (gradient elution: 0~5 min, 50/50; 5~10 min, 60/40; 10~15 min, 70/30; 15~20 min, 80/20; 20~30 min, 100% CH 3 CN); flow rate, 0.5 mL•min -1 ; temperature, 30°C; detection wavelength, 542 nm; and injection volume, 10.0 μL; (b) The reaction mechanism of NR-BS4 towards H 2 S 2 ; (c) HRMS spectrum of the probe NR-BS4 reacted with H 2 S 2 .

Fig. 8 .Fig. 9 .

 89 Fig. 8. Confocal fluorescence images of exogenous H 2 S 2 in HepG2 cells. HepG2 cell incubated by probe NR-BS4 (5.00 μM) and observed under red channel (a), bright field (b), overlay (c), then further incubation with Na 2 S 2 (25.0 μM, 100 μM and 180 μM) for 15 min and observed under red channel, bright field, overlay.Furthermore, the imaging capacity of NR-BS4 to monitor endogenous H 2 S n was also investigated. It has been reported that lipopolysaccharide (LPS) can increase H 2 S n production by inducing CSE mRNA overexpression. Obviously, a dramatical enhancement in intracellular fluorescence intensity can be discovered from Fig.9a-c, compared to that of Fig.8a. In addition, when DL-propargylglycine (PAG, a CSE inhibitor) was added, the fluorescence intensity was suppressed. These results are consistent with those reported in the literature that CSE contributes to generate endogenous H 2 S n . In conclusion, NR-BS4 enables the detection of endogenous H 2 S n levels in living cells.

3. 8 .

 8 Fluorescence imaging of H 2 S n in zebrafish In view of the satisfactory performance of NR-BS4 in detecting H 2 S n in cells, the applicability of the probe NR-BS4 in live zebrafish is chosen to be evaluated. As shown in Fig. 10a, samples loaded with probe NR-BS4 display medium fluorescence brightness, which was caused by H 2 S n in zebrafish. Probe-loaded zebrafish embryos served with external Na 2 S 2 displayed increased fluorescence (Fig. 10b). These results further demonstrate the promise of probe NR-BS4 for in vivo imaging.

Fig. 10 .

 10 Fig. 10. Fluorescence images of H 2 S 2 in zebrafish. 5.00 μM of NR-BS4 under red channel (a), bright field (b); (c) 5.00 μM of NR-BS4 incubated with 350 μM of Na 2 S 2 for 30 min under red

  D 5 N) δ 181.46, 152.63, 151.64, 147.15, 137.82, 136.53, 134.48, 132.70, 132.38, 131.66, 130.97, 128.36, 127.71, 125.65, 125.59, 125.14, 117.28, 112.37, 110.56, 105.21, 104.17, 96.45, 44.97, 12.39. HRMS (C 26 H 21 N 3 O 7 S): calcd. For [M+H] + 520.1178; found: [M+H] + 520.1071.

	1 H NMR (400 MHz, C 5 D 5 N) δ 8.52 (s, 1H), 8.17 (d, J = 7.9 Hz, 1H), 8.07 (d, J = 8.0
	Hz, 1H), 7.86 -7.69 (m, 3H), 7.65 (d, J = 7.7 Hz, 2H), 6.76 (d, J = 8.9 Hz, 1H), 6.58
	(d, J = 1.4 Hz, 2H), 3.36 (t, J = 16.7 Hz, 4H), 1.10 (t, J = 7.0 Hz, 6H); 13 C NMR (101
	MHz, C 5

Acknowledgements

The authors thank the National Natural Science Foundation of China (No. 21572177), the Key Research and Development Program of Shaanxi Province (No. 2023-YBSF-396), Biomedicine Key Laboratory of Shaanxi Province (No. 2018SZS41).