Kia Dashtipour
email: kia.dashtipour@glasgow.ac.uk

William Taylor

Shuja Ansari

Mandar Gogate

Adnan Zahid

Yusuf Sambo

Amir Hussain

Qammer H Abbasi

Muhammad Ali Imran

Public Perception towards fifth generation of cellular networks (5G) on social media

Keywords: Sentiment Analysis, 5G, Mobile Network Quality, Machine Learning, Opinion Mining

With advancement of social media network, there are lots of unlabelled reviews available online, therefore its necessarily to develop an automatic tools to classify these types of reviews. For utilising these reviews for user perception, there is a need for automated tools that can process online user data for optimising user perception. In this paper, a sentiment analysis framework has been proposed to identify people's perception towards mobile networks. The proposed framework consists of three basic steps: preprocessing, feature selection and applying different machine learning algorithms. The performance of the framework has taken into account different feature combinations. The simulation results show that the best performance is by integrating unigram, bigram and trigram features.

INTRODUCTION

The 5th generation mobile network is new global wireless standard after 1G, 2G, 3G and 4G network. The 5G is new types of network which is designed and developed to connect virtually everyone and everything together and consists of different machines, objectives and devices. In addition, the fifth generation of cellular networks is required to be more efficient and economical in terms of Key Performance Indicators (KPI). KPIs are of interest to stakeholders and different applications. These KPIs from an operator perspective, consist of capacity, reliability and quality of service. From the user perspective, the KPIs include uninterrupted connection, infinite capacity and zero latency. However, there no technology that can offer infinite capacity or zero latency. In the past few years, there has been lots of research carried out in next generation mobile network (5G), which consists of different opportunities and challenges. There are different challenges in fifth generation of cellular networks (5G) that have been discussed in different literature. The most significant of these are ultra-dense networks and millimetre waves, however, there are other technologies which are significant for the next generation of network such as two-layer architecture and cognitive radio based architectures which have great performance in the next generation of the network.

In fifth generation of cellular networks (5G), network data analytic and machine learning systems can perform a key role. The technique to understand people's behaviour towards the 5G network is vital and it helps to improve the performance of the network communication [START_REF] Sharma | Twitter sentiment analysis for brand reputation of smart phone companies in india[END_REF][START_REF] Opincariu | Education in the 5g and the ai context[END_REF].

With the advent of social media and e-commerce, websites allow users to share opinions and feedback about different products and services. Customers can make important decisions by reading other people's experiences. In addition, the customer feedback can be classified to make improvements on the service or product. For example, if a person wants to buy a mobile phone and the reviews provide negative information related to the battery, operation speed or camera, this can influence the consumers decision. In addition, this can assist in providing better mobile quality by taking into account the complaints made by past customers and making informed improvements to products. As another example, if someone wants to book a hotel, the buyer can look over the online reviews to understand previous customer experience such as cleanliness and services for the hotel. However, there are billions of bytes of data generated per day consisting of user feedback which cannot be manually labelled and analysed for individual organisations and companies [START_REF] Yadav | Sentiment analysis using deep learning architectures: a review[END_REF]. Sentiment analysis is the process of automatically understanding and classifying the data into positive and negative information from the source material such as reviews and comments. The main task of sentiment analysis is to assign polarity into sentences (positive or negative).

However, the online review is a mixture of positive and negative comments about different aspects of the products or services instead of expressing positive or negative opinions. For example, "the 5G mobile network is extremely fast, however I do not feel secure while I am using the 5G network". The sentence expresses positive sentiment towards the speed of the mobile and negative sentiment towards the security of the mobile network [START_REF] Kumar | Systematic literature review of sentiment analysis on twitter using soft computing techniques[END_REF], [START_REF] Dashtipour | A hybrid persian sentiment analysis framework: Integrating dependency grammar based rules and deep neural networks[END_REF].

Most of the current sentiment analysis approaches are focused on analysing products and movie reviews and there is less work been carried out in different fields such as people's perception towards fifth generation of cellular networks (5G). However, most of the current approaches consider a small corpus, which makes the task difficult for machine learning approaches to identify the overall polarity of the sentence. Furthermore, the current sentences consist of lots of sarcastic and ironic words which is difficult to determine the overall polarity for these types of sentences. For example, "Tell me something that I do not know". Most of the current approaches for sentiment analysis fail to understand the real noisy text consists of sarcasm, idioms, informal words and sentences with spelling mistakes. In addition, there are scarce availability of tools and resources.lexicon and labelled corpus are some of the tools which are available for sentiment analysis. This limited range of available tools is the main bottleneck in the design of the sentiment analysis approaches [START_REF] Kaity | An automatic non-english sentiment lexicon builder using unannotated corpus[END_REF]. One of the main issue for sentiment analysis approaches is lack of labelled dataset. However, it is worth to mention that, there are lots of unlabelled dataset available online, but it is time consuming for users to manually label dataset.

In order to address the aforementioned issues and limitations, framework that exploits n-gram features has been proposed to identify the polarity of sentence. This proposed approach demonstrated that the overall performance and effectiveness of polarity detection in real noisy data. The ngram features are based on the linguistic text rules that allow to extract text features from sentences. As a result, the ngram features take into account the relation between keywords and the word order and individual word polarity to determine the underlying polarity of the sentence. We perform an extensive and comprehensive set of experiments using novel corpus and compare the performance of the approach with different selected features. The Support Vector Machine(SVM), logistic regression, Naive Bayes and Multi Layer Perceptron (MLP) used to evaluate the performance of the approach. The comparative simulation results show that the proposed approach achieved better performance as compared to the state-of-art approach.

The rest of the paper is organised as follows: In Section 2 related work is presented, Section 3 presented the proposed framework, Section 4 presented the experimental results, Section 5 presents discussion and finally Section 6 concludes the work and presents the future work.

RELATED WORK

The extensive research in the current literature shows that the machine learning has been used in different fields such as sentiment analysis [START_REF] Hussain | Artificial intelligence-enabled analysis of public attitudes on facebook and twitter toward covid-19 vaccines in the united kingdom and the united states: Observational study[END_REF]; [START_REF] Dashtipour | A novel context-aware multimodal framework for persian sentiment analysis[END_REF][START_REF] Dashtipour | Multilingual sentiment analysis: state of the art and independent comparison of techniques[END_REF]Dashtipour et al. ( ,a, 2017c,b,a),b,a); [START_REF] Jiang | Robust visual saliency optimization based on bidirectional markov chains[END_REF]; [START_REF] Dashtipour | Exploiting deep learning for persian sentiment analysis[END_REF][START_REF] Dashtipour | A hybrid persian sentiment analysis framework: Integrating dependency grammar based rules and deep neural networks[END_REF]; [START_REF] Guellil | A semisupervised approach for sentiment analysis of arab (ic+ izi) messages: Application to the algerian dialect[END_REF]; [START_REF] Dashtipour | Persent 2.0: Persian sentiment lexicon enriched with domain-specific words[END_REF], cyber-security Adeel et al. (2019a); [START_REF] Jiang | A survey on deep learning for the routing layer of computer network[END_REF]; [START_REF] Shiva | Complex-valued computational model of hippocampal ca3 recurrent collaterals[END_REF]; [START_REF] Gogate | Random features and random neurons for brain-inspired big data analytics[END_REF], hand-written recognition Ahmed et al. (2021), speech enhancement Gogate et al. (2020aGogate et al. ( ,b, 2019aGogate et al. ( , 2017a[START_REF] Gogate | Dnn driven speaker independent audio-visual mask estimation for speech separation[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]; [START_REF] Adeel | Contextual deep learning-based audio-visual switching for speech enhancement in real-world environments[END_REF][START_REF] Adeel | Deep neural network-based contextual recognition of arabic handwritten scripts[END_REF]; [START_REF] Gogate | A novel brain-inspired compression-based optimised multimodal fusion for emotion recognition[END_REF], posture detection [START_REF] Taylor | An intelligent non-invasive real-time human activity recognition system for next-generation healthcare[END_REF]; [START_REF] Liaqat | A hybrid posture detection framework: Integrating machine learning and deep neural networks[END_REF][START_REF] Liaqat | Non invasive skin hydration level detection using machine learning[END_REF] and etc. However, there is not any research carried out to detect sentiment polarity for tweets related to 5G.

In the literature, extensive research has been carried out to implement different sentiment analysis approaches. The microblogging websites are the biggest platform that allows users to share their thoughts and opinions in the public domain. Twitter is the most well-known microblogging website that allows people to express their feelings and emotions in the form of "tweets" with a character range of 280. There are over 250 million tweets expressing the feelings and emotions of people with different opinions and situations [START_REF] Duong | Ultra-dense networks for 5G and beyond: modelling, analysis, and applications[END_REF] [START_REF] Mamgain | Sentiment analysis of top colleges in india using twitter data[END_REF].

The twitter users vary from politicians to everyday people that provide different types of reviews from different points of views. This is the the main reason a dataset collected from twitter is used in this paper.

There is lots of research carried out in this domain to determine people's perception towards different products such as Sony mobile. For example. [START_REF] Sharma | Smartphone-based fundus camera device (mii ret cam) and technique with ability to image peripheral retina[END_REF] focused to find the most well known smartphones in India, the tweets are collected and then machine learning is applied to determine a brand reputation score. This will help customers to find the most branded smartphones in India. [START_REF] Somula | Twitter sentiment analysis based on us presidential election 2016[END_REF] proposed an approach to perform a sentiment analysis to determine the winner of the US election in 2016, the tweets are collected with respect to Donald Trump and Hillary Clinton, the tweets analysing results reveals that Donald Trump received more positive scores as compared to Hillary Clinton. [START_REF] Mehta | Sentiment analysis of tweets using supervised learning algorithms[END_REF] proposed a sentiment analysis approach to identify the best Indian airlines through twitter, the analysis of results reveals that the customers are happier with airIndia services compared to spicejet. [START_REF] Kumari | Sentiment analysis on aadhaar for twitter data-a hybrid classification approach[END_REF] proposed an approach using twitter API to collect corpus, after pre-processing and use of natural language processing, the hybrid classifier utilised machine learning and long short-term memory to improve the performance of the approach. The experimental results reveal that the proposed model achieved better performance as compared with state-of-the-art approaches. [START_REF] Usama | Deep learning based weighted feature fusion approach for sentiment analysis[END_REF] introduced a novel model multi-level feature extraction and feature combination by using convolutional neural network (CNN) and recurrent neural network (RNN) to identify the sentiment in movie reviews.

The CNN and RNN received sentiment text as input and learn different features to network architecture.

The word embedding fed into CNN and learn multilevel contextual features from every layer of CNN and perform multilevel features fusion. Finally, the multilevel and multitype features are combined and softmax classifier is used to identify final polarity of sentences. Most of the aforementioned studies implement a lexicon to determine the polarity of the text. However, the use of lexicon fails to identify the polarity of word order in the sentence. In addition, most of the current studies use rules to detect negation in a sentence which cannot be directly applied to English language. However, we need a framework to identify the polarity of the sentence without developing a lexicon which is time consuming. Therefore, we implemented

METHODOLOGY

This section describes our proposed novel context-aware framework for fifth generation of cellular networks (5G) sentiment analysis. The proposed framework exploits the polarity of sentence more accurately as compared to traditional word occurrence frequency-based approaches. 1 shows the related keywords for fifth generation of cellular networks (5G).

Pre-processing: The tweets corpus has been collected using twitter API and it has been labelled into positive and negative using SentiWordNet. The corpus is divided into a training set (60%), test set (30%)

and validation set (10%) to apply machine learning algorithms including SVM, Naive Bayes and MLP. The corpus is tokenized and normalised. The tokenisation technique is used to break sentences into words. For example, "I really like mobile" will be converted into words such as "I", "really", "like" and "mobile".

Afterwards, the normalisation technique is used to normalise the tweets. For example, "The speed for 5G mobile is gr8" will be converted into "The speed for 5G mobile is great".

Ngram: N-gram features are widely used in different approaches of sentiment analysis. When there is one term is taken as feature is called unigram, for two term is called bigram and three terms is called trigram. In our proposed approach we have been using unigram, bigram and trigram and we used the combination of n-gram features.

SentiWordNet: In order to assign sentiment polarity into sentence we used SentiWordNet widely avaiable online lexicon to assign sentiment polarity (-1, 0, 1) into sentences.

Machine learning classifiers:

In order to evaluate the performance of the approach, the machine learning classifier is used to evaluate the performance of the approach. The scikit-learn python package is used to developed multi-layer perceptron, logistic regression, linear SVM, RBF SVM and naive bayes is used to train the model. The MLP consist of one hidden layer which is able to apply to supervised problem, the MLP are set of input and put and it learns to model the correlation between input and output. However, the main issue with MLP it consists of many parameters which is fully connected and each nodes is connected to other node which can result in redundancy and inefficiency. The main advantage of Naïve Bayes is performs fast and save lots of time however, the prediction can be wrong. In addition, the main advantage of logistic regression is easy to implement and train however, the number of observation is less than number of features. Finally, the main advantages of SVM is work well with unstructured data such as text, as the tweets are unstructured, therefore, the SVM performs well of tweets data. However, the main issue with SVM, the choosing correct kernel is not easy.

EXPERIMENTAL RESULTS

In order to calculate the sentiment polarity of the sentence, the SentiWordNet has been used to calculate overall polarity of the tweets. The tweets extracted using different keywords such as 5G, next generation mobile network, fifth generation of technology and 5g devices. The tweets are collected. After tweets collection, the punctation and stop words are removed and sentence are normalised, and then its been converted into BOW and finally machine learning classifiers trained. In the pre-processing stage, the sentence are normalised, for example, the word "fishing" are converted into "fish".

In order to evaluate the performance of the proposed approach, the tweets are converted into bag of words (BOW). These BOWs are sent to machine learning algorithms including Linear and RBF SVM, naive bayes, logistic regression and MLP to evaluate the performance of the approach. The intial experimental results demonstrate that the combination of unigram and bigram achieved accuracy of 86.71%.

Dataset: In order to evaluate the performance of the approach the tweets have been collected from twitter, the 5G hashtag has been used to collect tweets, more than fifty thousand tweets have been collected and the positive and negative polarity has been assigned into tweets using SentiWordNet lexicon. The neural tweets have been eliminated. The experimental results show that the combination of unigram and bigram achieved better performance as compared to other approaches. In order to evaluate the performance of the proposed approach, there are different evaluation metrics including accuracy, precision, recall and f-measure are used:

P recision = T P T P + F P

(1)

Recall = T P T P + F N (2)
F measure = 2 * P recision * Recall P recision + Recall (3) where TP denotes true positive, TN presents true negative, FP is false positive, and FN represents false negative respectively. In addition, Table 2 shows the parameters that are used to trained the machine learning methods. The scikit-learn package is used to train the machine learning classifiers. In addition, the training time for each models has been presented in Table 2.

Accuracy = T P + T N T P + T N + F P + F N (4)
Table 3 shows the results of different N-gram features. The unigram (Uni), bigram (Bi) and trigram (Tri) is extracted from the sentence, as the comparitive experimental result shows the unigram (Uni) achieved better performance as compared to other features.

Table 4 shows the comparison of different N-gram features. The empirical results shows that the combination unigram (Uni) and bigram (Bi) is achieved better performance as compared to other features. which it might cause cancer, the price of the 5G mobile which is very expensive and also the users concerns about the performance of 5G mobiles. In order to find the most negative keywords towards 5G the word frequency of the words negative sentences is calculated. The word frequency shows that the most discussed keywords are radiation, price and performance. As Figure 3 (a) shows the most twitter users who located in England, Scotland believe that the 5G can cause cancer. It is to be noted that, 31% of tweets in Scotland and 31% in England has negative concern about the cause of radiation using 5G. In addition, the Figure In contrast, the Figure 4 displays the overall trends for the twitter users. As the Figure 4 the most discussed trend in agriculture (29%), healthcare (28%) and smart home (26%).

The Figure 5 displays the most discussed trends towards 5G technology, the radiation, price and below technology towards 5G. For example, most users concerns towards 5G signal which it might cause cancer, the price of the 5G mobile which is very expensive and also the users concerns about the low technology of 5G mobiles. As shown in graph 5, England (a) the most discussed trends are agriculture (31%), healthcare (28%) and smart-home(16%). In addition, Scotland (b) the most discussed trends are agriculture (41%), healthcare (32%) and transport (12%). Additionally, in Wales the most discussed trends are agriculture (29%), healthcare (28%) and transport(18%). Finally, North Ireland, the most discussed trends are agriculture (29%), healthcare (28%) and transport (18%).

The graph 6 displays the occupation of the twitter users who had the most positive comments towards fifth generation of cellular networks (5G).

The graph 7 displays the occupation of the twitter users who had the most negative comments towards fifth generation of cellular networks (5G). As shown in Table 5, the top 10 positive and negative bigrams keywords related to next generation mobile network (5G) are presented. As the Table III shows the tweets towards price of 5G network generation is positive and towards EU countries are negative.

As shown in Table 6, the top 10 positive and negative trigram keywords related to next generation mobile network (5G) are presented. As the Table IV shows the tweets towards price of 5G network generation is positive and towards EU countries are negative.

DISCUSSION

In this study, the tweets related to the next generation mobile network (5G) were analysed and their sentiment polarity were identified. The sentiment analysis of 5G keywords tweets considered because these tweets carried lots of information related to next generation of communication network. Only English tweets were considered because it has large coverage and the most widely common language used in the world. In the current study, more than 10,000 tweets were retrieved. There were many duplicate tweets in the retrieved data which were removed from the database. However, we did not limit our dataset to tweet Moreover, as part of the data pre-processing we have removed the emoji characters from the tweets.

However, it is worth mentioning that emoji characters are commonly used in tweets and they are useful for identifying the overall polarity of the tweets. In future work, we intend to consider emoji characters as part of the process to identify the overall polarity of tweets as the emoji characters can provide more accurate sentiment scores. In contrast, the time frame to analyse the specific tweets related to fifth generation of cellular networks (5G) might be associated with events which increases the number of tweets in certain dates, weeks or months. It is worth to mention that, there are lots of tweets related to conspiracy theories, for example some of tweets believed that the 5G can cause covid-19 in people or it can cause cancer.

Herein we explain the most topics discussed which can change the overall sentiment polarity of the tweets into positive: The most discussed topics related to 5G was speed of the current network. Most of the tweets were positive towards the speed of 5G. However, it is to be noted that, there are large number of tweets were negative about the bandwidth and speech of the 5G mobile network.

Security: In addition, another most discussed topic related to 5G was security of the 5G services. Most of the twitter users were positive towards the current security towards the services for 5G. For example, "5G is more secure than 4G". Performance: Additionally, the performance of the 5G mobile network were discussed in most of the tweets. Most of the tweets were positive towards the performance of the 5G network communication. For example, "I am really excited with current performance of 5G mobile".

Herein we explain the most topics discussed which can change the overall sentiment polarity of the tweets into negative:

5G expose cancer: Our finding shows that, most of the tweets were discussed about next generation mobile network can cause cancer. For example, "I won't buy 5G mobile, because it can cause cancer".

There is strong negative comments towards the health issue towards 5G network communication. Most of the Twitter users believe that the 5G can cause damage to their health.

5G price: Our analysis shows that, most of the Twitter users have negative comments towards the price of the 5G mobiles. For example, one tweet clearly mentioned that "How can I afford to buy such expensive mobiles".

CONCLUSION

In this study, we perform a series of sentiment analysis on data retrieved from twitter. The twitter data under investigation was related to the fifth generation of cellular networks (5G). We have collected relevant tweets in the English language. Therefore, we proposed framework for mobile networks (such as 5G)

based on different feature combinations. The performance of the proposed framework is evaluated using different feature combination in terms of different evaluation metrics such as accuracy, precision, recall and f-measure. In addition, we have compared the proposed method with different machine learning algorithms such as Naïve Bayes, MLP, SVM, etc. In addition, we have analysed the tweets to understnad user perception towards 5G. As part of our future work, we intend to extend the current framework for multilingual sentiment analysis and integration of a closed loop self organising network algorithm with the proposed user sentiment analysis framework.

ADDITIONAL REQUIREMENTS

For additional requirements for specific article types and further information please refer to Author Guidelines.

Figure 1 .

 1 Figure 1. Proposed Framework for Sentiment Analysis for people perception towards fifth generation of cellular networks (5G)

Figure 2

 2 Figure 2 displays the positive sentiment towards fifth generation of cellular networks (5G) technology, the speed, security and performance towards 5G. For example, the user has a positive opinion towards speed, security and performance. In order to find the most positive keywords towards fifth generation of cellular networks (5G) the word frequency of the words positive sentences is calculated. The word frequency shows that the most discussed keywords are speed, security and performance. As shown in Figure 2 (a) Scotland and England have the most positive discussion towards speed. For example, 45% of tweets in Scotland and 30% in England has positive opinion about the speed of 5G. In the other hand, Figure 2 (b) demonstrate that England and Scotland have most positive discussion toward security trend. 41% of tweets in England and 36% in Scotland has positive opinion about the security of 5G. Figure 2 (c) shows Scotland and England have most positive discussion toward performance trend. 39% of tweets in Scotland and 28% in England has positive opinion about the performance of 5G.

3

  (b) most twitter users who located in Scotland believe that the price of 5G mobile is very expensive. It is worth to mention that, 32% of tweets in Scotland and 27% of tweets in Wales has negative concerns about the expensive price of 5G technology. Additionally, the Figure3(c) shows the most tweets users in North-Ireland are less satisfied with current performance of the 5G mobiles. It is to be noted that, 27% in North-Ireland and 25% in England has negative concern about current performance of 5G.

Figure 2 .

 2 Figure 2. Positive trends towards fifth generation of cellular networks (5G)

Figure 3 .

 3 Figure 3. Negative trends towards fifth generation of cellular networks (5G)

Figure 5 .

 5 Figure 5. Most discussed trends for fifth generation of cellular networks (5G)

Figure 6 .Figure 7 .

 67 Figure 6. Occupation for most positive twitter users towards fifth generation of cellular networks (5G)

Table 1 .

 1 Next Generation Mobile Network (5G)

	Keywords
	5G
	Next generation mobile network
	fifth generation of technology
	5G devices
	a framework for twitter sentiment analysis that integrates feature engineering and machine learning to
	improve the performance and robustness of polarity detection in real noisy data.

Table 2 .

 2 Paramets of ML algorithms

	Algorithm	Parameter		Time
	SVM		RBF Kernal		4 mins and 21 s
	Naive Bayes	sample weight = none 2 mins and 12 s
	MLP		activation = relu	3 mins and 31 s
	Logistic regression Penalty = l2		3 mins 42 s
	Table 3. Results of N-gram Features
	Feature Classifier	Accuracy Precision Recall F-score
	Uni	MLP	85.92	0.86	0.86	0.86
	Uni	LR	86.14	0.86	0.86	0.86
	Uni	Linear SVM 84.79	0.84	0.84	0.84
	Uni	RBF SVM	62.61	0.72	0.63	0.54
	Uni	NB	86.48	0.87	0.86	0.87
	Bi	MLP	79.16	0.8	0.79	0.78
	Bi	LR	78.37	0.79	0.78	0.78
	Bi	Linear SVM 73.98	0.78	0.74	0.72
	Bi	RBF SVM	62.61	0.72	0.63	0.54
	Bi	NB	78.82	0.8	0.79	0.78
	Tri	MLP	73.42	0.77	0.73	0.71
	Tri	LR	72.52	0.77	0.73	0.7
	Tri	Linear SVM 69.48	0.77	0.69	0.65
	Tri	RBF SVM	62.61	0.72	0.63	0.54
	Tri	NB	71.39	0.77	0.71	0.68

Table 4 .

 4 Comparison of combination of N-gram Features

	Feature Classifier	Accuracy Precision Recall F-score
	Uni + Bi MLP	86.71	0.87	0.87	0.87
	Uni + Bi LR	86.14	0.86	0.86	0.86
	Uni + Bi Linear SVM 85.92	0.86	0.86	0.86
	Uni + Bi RBF SVM	62.61	0.72	0.63	0.54
	Uni + Bi NB	85.81	0.86	0.86	0.86
	Uni + Tri MLP	85.13	0.85	0.85	0.85
	Uni + Tri LR	86.59	0.87	0.87	0.87
	Uni + Tri Linear SVM 86.48	0.87	0.86	0.87
	Uni + Tri RBF SVM	62.61	0.72	0.63	0.54
	Uni + Tri NB	85.47	0.85	0.85	0.85
	Bi + Tri	MLP	76.91	0.79	0.77	0.76
	Bi + Tri	LR	77.02	0.79	0.77	0.76
	Bi + Tri	Linear SVM 77.02	0.8	0.77	0.76
	Bi + Tri	RBF SVM	62.01	0.6	0.59	0.6
	Bi + Tri	NB	71.28	0.7	0.69	0.7
	Figure 3 displays the negative sentiment towards fifth generation of cellular networks (5G) technology,
	the radiation, price and below technology towards 5G. For example, most users concerns towards 5G signal

Table 5 .

 5 Most frequent positive bigrams for next generation mobile network (5G)

	Use cases
		Agriculture
	Healthcare	29.0%
	28.0%
	26.0%	6.0% 5.0% 6.0%	Transport VR/AR
		Factory
	Smarthome
	Figure 4. Trend for UK towards fifth generation of cellular networks (5G)
	Positive Bigram	Negative Bigram
	5G cheap	Low performance
	Good coverage	Awful services
	Good supply	blow Huawei
	Great performance	Hate 5G
	High security	Low speed
	Fast speed	Low coverage
	Great system	Low frequency
	communication networks 5G Crap
	Nokia performs	Slow 5G
	great satisfaction	Expensive technology

Table 6 .

 6 Most frequent positive Trigrams for next generation mobile network (5G)

	Positive Trigram	Negative Trigram
	cheap 5G phones	Slow coverage 5G
	Good coverage phone	Low suppliers services
	best 5G phones	blow Huawei China
	Good Supply 5G	5G slow connection
	Great areas coverage	5G low frequency
	Good test zones	About 5G dangers
	5G fast speed	Low frequency 5G
	5G good communication New 5G Crap
	great 5G signal	Expect 5G Slow
	Nokia performs well	Expensive New technology
	Speed:

Table 7 .

 7 Table 7 shows the example of positive and negative tweets towards the fifth generation of cellular networks Example of Positive and Negative tweets towards Next Generation Mobile Network(5G) Positive Negative Currently, The 5G phones are the best that you can buy right now

	with 5G as dangerous
	to our health

257 (5G).

258

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ACKNOWLEDGMENTS

This research work was funded by EPSRC DTG EP/N509668/1 Eng.

FUNDING

This research was funded under EPSRC DTA studentship (EPSRC DTG EP/N509668/1 Eng

CONFLICT OF INTEREST STATEMENT

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

AUTHOR CONTRIBUTIONS