Théo Thréard
email: threard.theo@gmail.com

Elton De Lima Savi

Samuel Raetz

Sergey Avanesyan

Nikolay Chigarev

Vincent Tournat

David H Hurley

Vitalyi Gusev

IMAGING GRAINS IN A POLYCRYSTAL IN 3D WITH NANOACOUSTIC WAVES: SIGNAL PROCESSING METHODS

Picosecond laser ultrasonics methods provide non-contact, non-invasive imaging of µm samples, serving in biology to map cell mechanics, likewise in material science to scan microstructures. This work presents the 3D reconstruction of the cubic polycrystalline structure of ceria (CeO 2 ), with the time-domain Brillouin scattering (TDBS) method. We report on the development of signal processing methods to process the TDBS signals obtained by a scan of a micrometric area of ceria. 3D volumetric reconstruction of the grains is obtained with a maximum depth of 10 µm. Imaging contrast is enhanced thanks to the detection of quasi-shear acoustic waves, distinguishing small grains with slightly different crystalline orientations.

INTRODUCTION

Time-domain Brillouin scattering (TDBS) [START_REF] Thomsen | Coherent Phonon Generation and Detection by Picosecond Light Pulses[END_REF][START_REF] Gusev | Advances in applications of time-domain Brillouin scattering for nanoscale imaging[END_REF] is a pumpprobe technique suited to the imaging of transparent materials like polycrystals [START_REF] Wang | Nondestructive characterization of polycrystalline 3D microstructure with timedomain Brillouin scattering[END_REF]. In an opto-acoustic transducer (OAT), the pump fs laser pulse induces, via thermoelastic generation, an acoustic wave. When this wave is transmitted to the elastically anisotropic (cubic symmetry) ceria, up to three mode-converted acoustic pulses could propagate. The delayed probe fs laser pulse is strongly reflected at the fixed OAT surface, and weakly reflected at the propagating acoustic wavefronts in the optically isotropic grains of ceria. The interferences between these reflections produce, in the reflectivity signal collected by a photodetector, the so-called Brillouin oscillations which frequencies depend on the propagating acoustic pulses' velocities. Monitoring in time the Brillouin frequency changes of each acoustic mode, i.e. the velocity changes due to propagation in differently-oriented grains, give access up to three different views of the 1D distribution of the grains along the z-direction (depth). The 3D reconstruction is obtained by gathering 1D distribution from a 1 µm-step 60 × 60 µm 2 scan (xy-directions) of the sample, performed with an asynchronous optical sampling (ASOPS) system.

BRILLOUIN FREQUENCIES & PROCESSING

The three quasi acoustic modes that can be launched in ceria are: a quasi-longitudinal acoustic (LA) and two quasishear acoustic (TA) modes, labelled "slow" (STA) and "fast" (FTA). The velocity v of the acoustic wave propagating in the transparent sample are linked to the Brillouin oscillations frequency f BO , at normal incidence of the probe beam, via the equation:

f BO = 2 n v λ , (1)
with n the refractive index of ceria and λ the probe laser wavelength. Estimation of the expected Brillouin frequency (BF) of these three possible acoustic modes are given in Table 1 using the elastic properties in Ref. [ 1. Expected Brillouin frequency (BF) intervals to be detected in the case of cubic ceria (CeO 2 ) for each three acoustic modes.

Typical raw transient reflectivity signals measured via a photodetector are first subject to a filtering in order to remove the thermal background, as well as for de-noising. The 3600 acoustic signals obtained are subject to a signalby-signal and mode by mode procedure in 2 steps: (i) the slicing and (ii) the shaping. The slicing consists in estimating the instantaneous BF of the signal in a short-time Hanning window. The strongest frequency component in the Fourier transform (FT) of the windowed acoustic signal is estimated by applying a synchronous detection technique (SDT) [START_REF] Kuriakose | Longitudinal sound velocities, elastic anisotropy, and phase transition of high-pressure cubic H 2 O ice to 82 GPa[END_REF]. The instantaneous frequency is converted into instantaneous acoustic wave velocity using Eq. (1). Finally, each acoustic signal is converted into a vector containing the estimated BF with varying depth steps. Gathering these 3600 BF vectors leads to a final 4D (3D for spatial coordinates and 4D with the associated BF) sliced representation of the sample's grains. The shaping then consists in a segmentation of the BF points forming the 4D sliced representation, using Otsu's image segmentation method [START_REF] Otsu | A Threshold Selection Method from Gray-Level Histograms[END_REF]. The BF points that belongs to a common grain are aggregated in point clouds that are used as input of an alphashape algorithm [START_REF] Edelsbrunner | On the shape of a set of points in the plane[END_REF], allowing a 3D volumetric reconstruction of the grains (Fig. 1).

RESULTS & DISCUSSION

Each acoustic mode is differently sensitive to grain's orientation change. This imaging contrast effect is depicted in Fig. 2. The very first slice of the slicing is represented for each acoustic mode. Grains that appear as homogeneous when looking at the LA slice [Fig. 2 Reconstruction of the 3D grains obtained by the detection of TA modes not only agree with the grain boundaries shown by the LA mode, but brings also light on boundaries in the depth of the sample invisible to the LA mode. The ability of time-frequency tools to estimate the angle of an inclined buried interface between two differently oriented grains is one example of the wide range of information accessible from the presented results. Interestingly, grain boundaries can be identified via abrupt diminishing of the TDBS signals and by the BF shifting induced by the velocity change when the acoustic wave is transmitted from one grain to another.

CONCLUSION

The TDBS technique is applied on an interesting playground material, polycrystalline ceria, and signal processing methods are developed to reconstruct a 3D image of the sample's grains. For the first time, a complete 3D image of the scanned area is reconstructed, down to a depth of 10 µm and 3D grain boundaries are visualised. The contribution of the TA modes processing to the final 3D image highlights how crucial quasi-shear waves monitoring is for a complete characterisation of the sample. These results pave the way to TDBS imaging characterization, such as the analysis of buried inclined boundaries or else the determination of grain orientation. Being able to get such characteristics would bring major information on the roughness of the grains interfaces that could explain macro-scale behaviours, such as ionic conductivity in the case of ceria.

Figure 1 .

 1 Figure 1. 3D reconstruction of the grains imaged with the LA mode contribution to the TDBS signal in three different viewing angles.

  (a)], are in fact composed of smaller grains only visible when processing the two TA modes BF intervals [Fig. 2(b-c)]. Thanks to this complementary information, a good agreement with the electron backscattered diffraction (EBSD) pattern [Fig. 2(d)] of the scanned area is demonstrated.

Figure 2 .

 2 Figure 2. First slices obtained for the two types of acoustic mode: (a) LA mode, (b) TA mode (in the frequency interval [27, 37] GHz) and (c) TA mode (in the frequency interval [37, 44] GHz), (d) EBSD pattern of the scanned sample area.

10.48465/fa.2020.0548

e-Forum Acusticum, December 7-11, 2020

ACKNOWLEDGEMENTS

This work was supported by the Région Pays de la Loire through the RFI Le Mans Acoustique (project "Paris Scientifique OPACOP 2018"). The author thanks Z. Hua for providing the EBSD data. We thank our colleagues from NETA who provided insight and expertise that greatly assisted the research.


Kia Dashtipour
email: k.dashtipour@napier.ac.uk

Mandar Gogate
email: m.gogate@napier.ac.uk

Alexander Gelbukh
email: gelbukh@gelbukh.com

Amir Hussain
email: a.hussain@napier.ac.uk

Adopting Transition Point Technique for Persian Sentiment Analysis

Keywords: Sentiment Analysis, Persian, Machine Learning

Sentiment analysis is used to analyses people's opinions, views and emotions towards different entities such as products, organizations, companies and events. People's opinions are important for most others during their decision-making process. For example, if someone wants to buy a product, they might want to know more about that product and the experiences of others with that product. Sentiment analysis is able to classify the reviews based on their polarity; even if reviews are expressed in a sentence or document, sentiment analysis is used to classify it into positive, negative or neutral reviews. In this paper, we proposed a framework using TF-IDF and transition point to detect polarity in Persian movie reviews. The proposed approach has been evaluated using different classifiers such as SVM, Naive Bayes, MLP and CNN. The experimental results show the transition point is more effective in comparison with traditional feature such as TF-IDF.

I. INTRODUCTION

With the emergence of Internet, people is capable to share their comments and opinion using social network such as Twitter, Facebook, etc. Internet allow people to debate and share their opinions on different topics [START_REF] Gogate | Cochleanet: A robust language-independent audio-visual model for real-time speech enhancement[END_REF]- [START_REF] Ahmed | Offline arabic handwriting recognition using deep machine learning: A review of recent advances[END_REF]. There has been interest from companies and organisations in classifying opinion to obtain useful information about their product and services. In other words, it identifies whether a sentence is positive or negative [START_REF] Dashtipour | Multilingual sentiment analysis: state of the art and independent comparison of techniques[END_REF]- [START_REF] Adeel | Lip-reading driven deep learning approach for speech enhancement[END_REF]. The sentiment analysis is useful for companies to automatically determines the polarity of the sentence. Sentiment analysis can identify the overall polarity of thousands of comments on short time of period. The evaluation of comments allows companies to improve their product and services. Thus, government used sentiment analysis to understand public's opinion about services which government provides for people . Sentiment analysis has been classified into predicted class such as subjective or objective, predict polarity which can be positive, negative or neutral or level of classification such as phrase, sentence or document and different approach can be applied such as supervised or unsupervised [START_REF] Jiang | Robust visual saliency optimization based on bidirectional markov chains[END_REF]- [START_REF] Cambria | Senticnet 6: Ensemble application of symbolic and subsymbolic ai for sentiment analysis[END_REF].

Sentiment analysis consist of two different approaches, the first approach is based on machine learning classifiers. It is used to trained classifier based on the predefined classes, this method is called supervised classification. The second method is based on the words along with their polarities (+1 to -1), the model calculates the polarity of the sentence from polarity of the word and assign final polarity into sentence, this is called unsupervised method (Joshi et al. 2017). The supervised classification has received high performance; however, it has some shortcomings. It required to build a trained dataset and labelled documents by human expert. The process is very time consuming and it is difficult to train sarcasm and idioms. Moreover, the model can be built for multi-domain purposed, in this case, the performance of the approach is become lower because trained data is not applied for different domain [?], [START_REF] Dashtipour | Exploiting deep learning for persian sentiment analysis[END_REF], [START_REF] Ahmed | Deep neural networkbased contextual recognition of arabic handwritten scripts[END_REF], [START_REF] Dashtipour | A comparative study of persian sentiment analysis based on different feature combinations[END_REF].

The lexicon based approach can be used to identify the polarity of the sentence, the lexicon based can be built manually which requires lots of effort or automatically which is fast and less human effort required [START_REF] Dashtipour | An ensemble based classification approach for persian sentiment analysis[END_REF]- [START_REF] Dashtipour | Public perception towards fifth generation of cellular networks (5g) on social media[END_REF]. The main challenges of sentiment analysis research are most of current approaches is used for English language. However, with growth of the Internet, people share their opinions and comments in different languages. Therefore, sentiment analysis is valuable for other languages such as Persian.

It is a difficult task for SA to understand the text structure for Persian language. For example, the following sentence, ("I do not like the movie direction, but I really like the acting"), contains positive and negative sentiment about the movie. Most of the current approaches are not able to detect the overall polarity of the sentence, the traditional system is unable to understand the different sentiment expressed in the sentence. Additionally, the presence of negation can flip the polarity from positive into negative. In addition, The online users can discuss various topics. For example, if they are talking about one product [START_REF] Dashtipour | Adaptation of sentiment analysis techniques to persian language[END_REF], [START_REF] Ozturk | A novel deep learning driven, low-cost mobility prediction approach for 5g cellular networks: The case of the control/data separation architecture (cdsa)[END_REF]. Alternatively, they can discuss about other product. Additionally, they can discuss about different features of the product.

, ("The charger of the Apple mobile is very good, but I do not like the charger for Apple laptop"). The sentence aspect is charger and the sentiment of the sentence for mobile is positive however, the second part of the sentence has a negative sentiment about the laptop [START_REF] Alqarafi | Toward's arabic multi-modal sentiment analysis[END_REF]- [START_REF] Dashtipour | Persent 2.0: Persian sentiment lexicon enriched with domain-specific words[END_REF].

There are lots of meaningful information is behind the text. For example, , "It was not a bad movie, but if they are using different cast it could be a better movie". The natural language processing is used to understand the text. The use of keywords, punctuation and frequency of words are very useful to understand the text. However, the increase of web content in different languages make the English algorithm inefficient. In the order to overcome these challenges in this thesis we focus on Persian SA. Most of the previous studies have only focused on English SA and therefore most of the tools and resources are available in English. Hence the tools and research, in the most of the other languages such as Persian, are comparatively less developed [START_REF] Dashtipour | A novel context-aware multimodal framework for persian sentiment analysis[END_REF]- [START_REF] Churcher | An experimental analysis of attack classification using machine learning in iot networks[END_REF]. Persian SA has the following main challenges: Lack of tools and resources: To the best of our knowledge, there is no valuable tools nor any online lexicon available for Persian language.

Utilizing many informal words: There are lots of informal words available in Persian language. Specifically, when online users share their opinion and comments online, they are using more of these informal words and phrases.

Lack of comprehensive approaches:

There is no valuable comprehensive approach available in Persian language. Most of the current approaches utilize the available approaches of English and translate the Persian dataset into English. Therefore, In this paper, the supervised machine learning approach used to identify the sentiment classification of Persian dataset and TF-IDF has been used with n-gram features, this information is more effective than frequency evaluated by our experiment results and transition point is used on n-gram features. The SVM, Naïve Bayes, MLP and CNN classifiers used to evaluate the performance of the proposed approach. This paper is organised as follows: Section II is related work of English and Persian sentiment analysis approaches, Section III is present methodology of supervised approach for Persian movie reviews, Section IV provides results and discussion and Section V is conclusion of the paper

II. RELATED WORK

With the growth of Internet, there are various types of online reviews are available such as product and movie reviews, these reviews are very helpful for companies. Sentiment analysis aim to classify and detect vital information from these reviews. In this section, the current Persian and English sentiment analysis has been summarised.

A. English

Agarwal et al. [START_REF] Agarwal | Conceptlevel sentiment analysis with dependency-based semantic parsing: a novel approach[END_REF] proposed concept parser based on the dependency relationship of words in the sentence, after filtering the words Mrmr feature selection has been used to train feature for machine learning classifiers, the proposed model used to classify the document into positive and negative. The movie reviews have been used to evaluate the performance of the approach, the overall accuracy is 88.9%.

Da Rocha et al. [START_REF] Da Rocha | Polarity classification on web-based reviews using support vector machine[END_REF] proposed a hybrid approach to identify the sentiment of the sentence, it consists of two phase feature selection method, the main step is pre-processing, then use of feature selection method, in order to evaluate the performance of the approach the movie review dataset has been used and SVM classifier has been trained. The overall performance of the approach is 81%.

Dashmukh et al. [START_REF] Deshmukh | Entropy based classifier for crossdomain opinion mining[END_REF] proposed a multidomain lexicon to predict the polarity of the sentence, the proposed algorithm used entropy with modified quantity instead of traditional entropy algorithm, the dataset contains different domains of product reviews, there are multiple experiments have been carried out to analyse the proposed approach, the overall accuracy of the proposed approach is 81.66% Sentiment analysis is used to classify document and sentence.

Hung et al. [START_REF] Hung | A performance comparison of feature extraction methods for sentiment analysis[END_REF] proposed an approach to classify the document based on the polarity, the document has been transferred into set of features such as unigram, bigram, trigram and part-of-speech tag and SentiWordNet has been used to assign polarity to features, the result of experiment display the bigram and trigram is performed better in comparison with other features.

Poria et al. [START_REF] Poria | Deep convolutional neural network textual features and multiple kernel learning for utterance-level multimodal sentiment analysis[END_REF] proposed approach to extract features from short texts, based on the inner layer of convolutional neural network, the extracted features of multimodal sentiment analysis have been used and then the feature vectors of text, audio and video has been combined, the proposed approach improved the performance by 14% and the overall accuracy of the approach is 88.60%.

B. Persian

Saraee and Bagheri et al. [START_REF] Saraee | Feature selection methods in persian sentiment analysis[END_REF] proposed a model sentiment classification of reviews documents in Persian, the model is based on pre-processing and feature selection and Naïve Bayes has been used to evaluate the performance of the approach, the mobile reviews have been used and overall performance of the approach is 81.02%.

Ebrahimi et al. [START_REF] Rashed | A supervised method for constructing sentiment lexicon in persian language[END_REF] proposed an approach to detect polarity in Persian sentences,the linguistic features have been extracted and mutual information has been used to identify the polarity of the sentence using SentiStrength of words. To evaluate the performance of the approach online reviews has been used, the result of SentiStrength has been compared with SentiWordNet and the overall accuracy is 80%.

Bagheri et al. [START_REF] Bagheri | Persian sentiment analyzer: a framework based on a novel feature selection method[END_REF] proposed sentiment classification approach for document level in Persian language, there are various feature types such as document frequency, term frequency has been used, and Naïve Bayes has been trained, the mobile reviews have been used to evaluate the performance of the approach, the overall accuracy of the approach is 0.907%.

III. METHODOLOGY

Pre-processing: The pre-processing step consists of cleaning text from unnecessary data, for example )It is really goodddd movie), the normalisation of data used to clean the data, removing stop-words is useful to improve the performance of the dataset, for example some words can be removed because these words do not contain any polarity such as , ) ,From, to, etc.).

N-grams:

The n-gram is sequence of n items in the text. The n-gram size one is called unigram, size two is bigram and size three is trigram. For example, "I went to school". The unigram is "I", "went", "to", "school", the bigram is "I went", "went to", "to school" and trigram is "I went to", "went to school".

TF-IDF: TF-IDF is stand for term frequency-inverse document frequency and it is statistical measure to calculate the importance of words in document, the importance of the words will be calculating with number of times which words appear in the document [START_REF] Chen | Turning from tf-idf to tf-igm for term weighting in text classification[END_REF].

The TF-IDF is consist of two parts, first part calculates the term frequency which is number of word appear in document divided by total number of words in the document and second term is inverse document frequency calculate the logarithm of number document divided by number of document which the term appears in them.

F requency = N umberof document totalnumberof term (1)
IDF: Inverse document frequency is measure the importance of the words in the whole documents, the following equation is used to weight the term.

F requency = N umberof document numberof documenttermappear (2)
Transition Point: The transition point is a frequency values to split the words in document into two different sets low and high. The transition point used to identify the medium frequency which is closely related to the conceptual content of the document.

IV. RESULTS AND DISCUSSIONS

Persian movie reviews Dataset: In order to evaluate the performance of the proposed framework the Persian movie reviews dataset has been used, the movie review contains 1000 positive and 1000 negative. The movie review has been collected from www.caffecinema.com and www.cinematicket.org, the movie reviews consist of movie reviews from 2014 to 2016.

In order to evaluate the performance of the proposed approach, the dataset is pre-processed, after normalisation and removing stop-words. The features such as unigram, bigram and trigram is extracted, then frequency, tf-idf and transition point used to train the SVM, Naïve Bayes, MLP and CNN. The Table 1 shows the result on TF-IDF and frequency (Freq) using different features such as unigram, bigram and trigram. The 10-fold cross validation used to train SVM, Naïve Bayes, MLP and CNN. The result shows the TF-IDF received better results in comparison with frequency.

In order to evaluate the performance of classification techniques, the following four evaluation metrics are used: precision, recall, f-measure and accuracy.

P recision = T P T P + F P

(3)

Recall = T P T P + F N (4)
F measure = 2 * P recision * Recall P recision + Recall (5)

Accuracy = T P + T N T P + T N + F P + F N (6)
The table V, VI, VII and VIII show the result for IDF transition point using n-gram features. There are SVM, Naïve Bayes, MLP and CNN classifiers is trained. The trigram features received better performance and unigram and bigram

V. CONCLUSION

In this paper, a supervised machine learning approach has been proposed for Persian using unigram feature with TF-IDF and frequency to identify the overall polarity of the Persian movie reviews. The experimental result show the TF-IDF is performed more effectively in comparison with frequency. The IDF transition point outperformed TF-IDF and frequency. In our future work, we will developed an approach to identify the overall polarity of multilingual sentences in Arabic, Persian and English.

TABLE I COMPARISON

 I TF-IDF AND FREQUENCY NAÏVE BAYES

	Features Precision	Recall Fscore Accuracy	Features Precision Recall Fscore Accuracy
	Unigram Freq	0.65	0.66	0.66	66.21	Unigram Freq Bigram	0.79	0.78	0.79	79.3
	Bigram Freq	0.66	0.65	0.66	66.94	Bigram Freq Trigram	0.80	0.80	0.80	81.96
	Trigram Freq	0.67	0.67	0.67	67.85	Unigram Freq Trigram	0.82	0.82	0.82	82.59
	Unigram TF-IDF	0.69	0.69	0.69	69.62	Unigram TF-IDF Bigram	0.83	0.83	0.83	83.95
	Bigram TF-IDF	0.70	0.71	0.72	71.39	Unigram TF-IDF Trigram	0.84	0.83	0.84	84.81
	Trigram TF-IDF	0.75	0.75	0.75	75.64	Bigram TF-IDF Trigram	0.85	0.84	0.85	85.91
					TABLE II
				COMPARISON TF-IDF AND FREQUENCY SVM
	Features Precision	Recall Fscore Accuracy	Features Precision Recall Fscore Accuracy
	Unigram Freq	0.69	0.68	0.69	69.8	Unigram Freq Bigram	0.81	0.80	0.81	81.46
	Bigram Freq	0.71	0.70	0.71	71.09	Bigram Freq Trigram	0.82	0.81	0.82	82.94
	Trigram Freq	072	0.71	0.72	72.52	Unigram Freq Trigram	0.83	0.82	0.83	83.63
	Unigram TF-IDF	0.72	0.72	0.72	72.51	Unigram TF-IDF Bigram	0.84	0.83	0.84	84.59
	Bigram TF-IDF	0.75	0.74	0.75	75.94	Unigram TF-IDF Trigram	0.86	0.85	0.86	86.93
	Trigram TF-IDF	0.78	0.77	0.78	78.67	Bigram TF-IDF Trigram	0.89	0.88	0.89	89.76
					TABLE III
				COMPARISON TF-IDF AND FREQUENCY MLP
	Features Precision	Recall Fscore Accuracy	Features Precision Recall Fscore Accuracy
	Unigram Freq	0.75	0.75	0.75	75.69	Unigram Freq Bigram	0.84	0.83	0.84	84.69
	Bigram Freq	0.77	0.76	0.77	77.92	Bigram Freq Trigram	0.85	0.86	0.85	85.67
	Trigram Freq	0.77	0.78	0.79	77.5	Unigram Freq Trigram	0.86	0.85	0.86	86.43
	Unigram TF-IDF	0.79	0.79	079	79.53	Unigram TF-IDF Bigram	0.88	0.84	0.86	86.59
	Bigram TF-IDF	0.80	0.79	0.80	79.82	Unigram TF-IDF Trigram	0.89	0.89	0.89	89.61
	Trigram TF-IDF	0.81	0.81	0.81	81.79	Bigram TF-IDF Trigram	0.91	0.91	0.91	91.84

Funding: Amir Hussain was supported by the UK Engineering and Physical Sciences Research Council (EPSRC)-Grants Ref. EP/M026981/1, EP/T021063/1, EP/T024917/1.


Kia Dashtipour
email: k.dashtipour@napier.ac.uk

Mandar Gogate
email: m.gogate@napier.ac.uk

Alexander Gelbukh
email: gelbukh@gelbukh.com

Amir Hussain
email: a.hussain@napier.ac.uk

Persian Sentence-level Sentiment Polarity Classification

Keywords: Sentiment Analysis, Persian, Machine Learning

Assigning positive and negative polarity into Persian sentences is difficult task, there are different approaches has been proposed in various languages such as English. However, there is not any approach available to identify the final polarity of the Persian sentences. In this paper, the novel approach has been proposed to detect polarity for Persian sentences using PerSent lexicon (Persian lexicon). For this, we have proposed two different algorithms to detect polarity in the sentence and finally SVM, MLP and Naïve Bayes classifier has been used to evaluate the performance of the proposed method. The SVM received better results in comparison with Naïve Bayes and MLP.

I. INTRODUCTION

Analysing of web content is becoming very important due to augmented communication via Internet, and the number of online opinions has been increased. Therefore, the sentiment analysis has been introduced to classify the data based on their polarity. The sentiment analysis is automated application that can be used in different field to structure the data [START_REF] Dashtipour | Persian named entity recognition[END_REF]- [START_REF] Shiva | Complex-valued computational model of hippocampal ca3 recurrent collaterals[END_REF]. Analyse of sentiment has been spread into different fields such as marketing, sport, movies, news etc. There are lots of opinions available online about different product and service, and sentiment analysis can help users to make better decision by recommending pros and cons of a product. For example, if someone decide to buy a laptop, they can read these online opinions and decide which laptop is better [START_REF] Gogate | Cochleanet: A robust language-independent audio-visual model for real-time speech enhancement[END_REF]- [START_REF] Nisar | Cognitively inspired feature extraction and speech recognition for automated hearing loss testing[END_REF]. It is noted that 90% of users' reads online reviews and opinions before they make buying decision [START_REF] Dashtipour | Exploiting deep learning for persian sentiment analysis[END_REF]- [START_REF] Dashtipour | A novel context-aware multimodal framework for persian sentiment analysis[END_REF]. The recent studies focused on issues in different types of opinions to determine the knowledge gaps and discover the future challenges [START_REF] Dashtipour | A hybrid persian sentiment analysis framework: Integrating dependency grammar based rules and deep neural networks[END_REF]- [START_REF] Dashtipour | Multilingual sentiment analysis: state of the art and independent comparison of techniques[END_REF].

There is huge amount of data available online in different languages and in unstructured form that need to be converted into useful form. To overcome this challenge most of approaches has been proposed in English language and other languages e.g. Persian. Where Persian language is less developed to overcome this challenge, therefore, this paper focused on identifying the polarity for Persian sentences [START_REF] Cambria | Senticnet 6: Ensemble application of symbolic and subsymbolic ai for sentiment analysis[END_REF]- [START_REF] Liaqat | Non invasive skin hydration level detection using machine learning[END_REF]. In this paper, the novel framework has been proposed to detect Persian sentences polarity. For these two algorithms has been proposed to detect polarity in the Persian sentences. The first algorithm detects part-of-speech (POS) tag in Persian sentences, it detects tag such as adjective, adverb, noun and verb in the sentence, the second algorithm is used to detect negation in the sentence, PerSent lexicon has been used to assign polarity to the detected POS features. Section II provides the related work, section III is provides the methodology, section IV is experiment and result and finally section V concludes the paper.

II. RELATED WORK

In this section, the related work for sentiment analysis will be discussed, there are various approaches to detect polarity in the sentence.

• Document level: In the document level, the task is to understand the overall polarity of the whole document. For example, the document contains various positive and negative comments about different product, the final polarity of the document will be identified. • Sentence level: The task is determining whether the expressed sentence is positive, negative or neutral [START_REF] Dashtipour | An ensemble based classification approach for persian sentiment analysis[END_REF], [START_REF] Alqarafi | Toward's arabic multi-modal sentiment analysis[END_REF]. • Aspect level: The task in aspect level is to identify the different aspect of the sentence, for example, ("I like the restaurant service but I did not like the food"), the tone of sentence is negative but some aspect is not negative, the aspect level extract these features from sentence, the "restaurant service" and "food" will be extracted from sentence.

Ikeda et al, [START_REF] Ikeda | Learning to shift the polarity of words for sentiment classification[END_REF] proposed an approach for detect polarity in English for movie reviews. First the data is collected, after pre-processing, there are different features such as unigram, bigram and trigram are selected. The proposed approach achieved accuracy of 76%.

Choi and Cardie etl at, [START_REF] Choi | Adapting a polarity lexicon using integer linear programming for domain-specific sentiment classification[END_REF] proposed an approach to identify the polarity of the sentence using novel technique which is used lexicon to find the characteristic of data directly. This method is used to find the relation and characteristic between words and sentences. The proposed system received better performance to detect negative sentences in compare to positive sentence. Alamoudi et al, [START_REF] Alamoudi | Sentiment classification and aspect-based sentiment analysis on yelp reviews using deep learning and word embeddings[END_REF] proposed a novel model based on the rating-based feature and evaluate features using English document sentiment analysis. There are n-gram features has been extracted to evaluate the performance of the system. The overall performance of the system was 93.24%.

Basiri et al. [START_REF] Basiri | Sentence-level sentiment analysis in persian[END_REF] addressed the problem of lack of resources in Persian by developing a lexicon and an automatically labelled sentence-level corpus. A Naive Bayes classifier was trained on the collected corpus to determine the polarity in short sentences. However, the proposed approach is lack of comparison results with the state-of-the-art approaches. In addition, Ebrahimi et al. [START_REF] Rashed | A supervised method for constructing sentiment lexicon in persian language[END_REF] proposed a method to detect polarity in Persian online reviews based on adjectives extracted from the sentence and translated SentiWordNet lexicon. However, the proposed method is limited to adjectives and it does not exploit nouns, adverbs and verbs that provide extra information on the underlying sentiment. Moreover, Razavi et al. [START_REF] Razavi | Word embedding-based approach to aspect detection for aspect-based summarization of persian customer reviews[END_REF] detect polarity based on extracted nouns and adjectives and a Persian lexicon. However, the proposed approach do not exploit the word order and hierarchical semantic dependency.

Recently, Zhao et al. [START_REF] Zhao | Weakly-supervised deep embedding for product review sentiment analysis[END_REF] proposed a model to detect sentiment in product reviews using deep learning classifiers. The words are first converted into an embedding vector representation and then a deep learning classifier is used to classify sentiments. The experimental results showed the effectiveness of their proposed CNN (87%) as compared to LSTM (82%). Sohangir et al. [START_REF] Sohangir | Big data: Deep learning for financial sentiment analysis[END_REF] proposed a framework to detect sentiment in the financial market. The StockTwits dataset was used, and feature selection method such as chi-square was used to select features. Finally CNN classifiers were used to evaluate the performance of the approach. The proposed framework obtained an accuracy of 70.88%. Wagh et al. [START_REF] Wagh | A twitter sentiment analysis using nltk and machine learning techniques[END_REF] proposed an approach to identify the sentiment in tweets and twitter API utilized to collect the tweets. Different classifiers such as Naive Bayes and Logistic Regression were used to evaluate the performance of the approach. Experimental results showed that logistic regression achieved better accuracy as compared to Naive Bayes. Pitsilis et al. [START_REF] Pitsilis | Detecting offensive language in tweets using deep learning[END_REF], proposed a framework to discern hateful content in social media using LSTM classifier. The authors collected data from Twitter and fed extracted features into an LSTM classifier. The results demonstrated the effectiveness of LSTM as compared to SVM.

The table I summarise some of the existing approaches of sentiment classification polarity for various languages.

III. METHODOLOGY

In this section, we will discuss about proposed framework to detect polarity in Persian language sentences. The figure below display the proposed framework to identify the polarity of Persian language sentences.

The algorithm shows in Fig 2 has been proposed to identify the final polarity of Persian sentence. The JHAZM part-ofspeech tag of tag has been used to identify the tag for Persian sentence.

The algorithm shows in Fig 3 has been proposed to identify the negation in the Persian sentence, if word is appeared in the sentence with positive polarity, change the overall polarity into negative and if this word is appearing with negative polarity change polarity into positive.

Pre-processing: The pre-processing step is consisting of tokenisation, normalisation and stop words removal. The tokenisation is processing of breaking texts into single tokens, normalisation is processing of removing noised in the text. For example, (The movie was greattttt) will be changed into (The movie is great) and stop word is removing unnecessary words such as (from, to etc.). PerSent lexicon: In order to identify the polarity of Persian sentences, the PerSent lexicon will be used, the lexicon contains 1500 Persian words along with their part-of-speech tag and their polarity [START_REF] Dashtipour | Persent 2.0: Persian sentiment lexicon enriched with domain-specific words[END_REF]- [START_REF] Hussien | Comparison of sentiment analysis approaches using modern arabic and sudanese dialect[END_REF].

Persian polarity: In order to calculate the overall polarity of the sentence, the noun, adjective, adverb and verb are extracted from the sentence. The tag such as determiners are discarded. The following equation has been used:

Sp = N p + V p + AV p + ADJp n (1)
Sp denote the sentence polarity, N-p denote the noun polarity, Vp denote verb polarity, AV-P is adverb polarity and ADJ-P denotes adjective polarity. For example, (The movie was great), NOUN, ADJECTIVE, -VERB. The adjective and verb is extracted and the PerSent lexicon will used to identify the polarity of the sentence, the formula number one has been used to calculate the overall polarity of the sentence. Once the polarity of the sentence has been identified the following rules has been used to identify the whether sentence is positive, negative or neutral.

If sentence polarity is greater than 0 then the polarity of the sentence is positive, if it is less than 0 is negative and equal to 0 is neutral.

IV. RESULTS AND DISCUSSIONS

Dataset: The Persian movie review dataset has been used to evaluate the performance of the proposed approach, the table below display the result of proposed approach. The JHAZM is Python tools for Persian NLTK contain part-of-speech tag, dependency parser, normalisation and stemming. The movie reviews contain 1000 positive and 1000 negative sentences. The movie has been collected from www.caffecinema.com and The adjective is performed better in comparison with other part-of-speech tag because the adjective is identified the polarity of the sentence. The weakness of proposed approach for Persian sentence polarity detection is not able to handle sarcasm in the sentence, it is required to develop system to detect sarcasm in the sentence, the further study is required to detect ironic and sarcasm in the sentence to improve the performance of the approach. The Table 4 display the example of Persian sentence polarity detection approach, the input sentences, English translation and final polarity of the sentence.

V. CONCLUSION

In this paper, a novel approach has been proposed to identify the polarity of the Persian sentences, the approach has been proposed to extract POS tag of the Persian sentence, then PerSent lexicon (Persian lexicon) has been used to assign polarity to extracted features and SVM, MLP and Naïve Bayes classifiers has been used to evaluate the performance of the approach. In the future work, we will develop an approach of multilingual polarity detection for Persian, Arabic and English.

Funding: Amir Hussain was supported by the UK Engineering and Physical Sciences Research Council (EPSRC)-Grants Ref. EP/M026981/1, EP/T021063/1, EP/T024917/1.

Fig. 1 .

 1 Fig. 1. Proposed Framework for Persian Sentiment Analysis