AI-Based Techniques for Online Social Media Network Sentiment Analysis: A Methodical Review
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32845
AI-Based Techniques for Online Social Media Network Sentiment Analysis: A Methodical Review

Authors: A. M. John-Otumu, M. M. Rahman, O. C. Nwokonkwo, M. C. Onuoha

Abstract:

Online social media networks have long served as a primary arena for group conversations, gossip, text-based information sharing and distribution. The use of natural language processing techniques for text classification and unbiased decision making has not been far-fetched. Proper classification of these textual information in a given context has also been very difficult. As a result, a systematic review was conducted from previous literature on sentiment classification and AI-based techniques. The study was done in order to gain a better understanding of the process of designing and developing a robust and more accurate sentiment classifier that could correctly classify social media textual information of a given context between hate speech and inverted compliments with a high level of accuracy using the knowledge gain from the evaluation of different artificial intelligence techniques reviewed. The study evaluated over 250 articles from digital sources like ACM digital library, Google Scholar, and IEEE Xplore; and whittled down the number of research to 52 articles. Findings revealed that deep learning approaches such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Bidirectional Encoder Representations from Transformer (BERT), and Long Short-Term Memory (LSTM) outperformed various machine learning techniques in terms of performance accuracy. A large dataset is also required to develop a robust sentiment classifier. Results also revealed that data can be obtained from places like Twitter, movie reviews, Kaggle, Stanford Sentiment Treebank (SST), and SemEval Task4 based on the required domain. The hybrid deep learning techniques like CNN+LSTM, CNN+ Gated Recurrent Unit (GRU), CNN+BERT outperformed single deep learning techniques and machine learning techniques. Python programming language outperformed Java programming language in terms of development simplicity and AI-based library functionalities. Finally, the study recommended the findings obtained for building robust sentiment classifier in the future.

Keywords: Artificial Intelligence, Natural Language Processing, Sentiment Analysis, Social Network, Text.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 496

References:


[1] X. Fang and J. Zhan, “Sentiment analysis using product review data”, Journal of Big Data, Vol. 2, No. 1, pp. 1 – 8, 2015.
[2] S. Dhuria, “Sentiment Analysis: An approach in Natural Language Processing for Data Extraction”, International Journal of New Innovations in Engineering and Technology, Vol. 2, NO. 4, pp. 27 – 31, 2015.
[3] S. Utz, P. Kerkhof, and J. V. Bos, “Consumers rule: How consumer reviews influence perceived truthworthiness of online stores”, Electronic Commerce Research and Application, Vol. 11, No. 1, pp. 49 – 58, 2012.
[4] L. K. Tan, J. Na, Y. Theng and K. Chang, “Sentence-Level Sentiment Polarity Classification Using a Linguistic Approach”, Digital Library for Cultural Heritage, Knowledge Dessemination and Future Creation Lectures Notes in Computer Science, pp. 77 – 88, 2011.
[5] N. C. Dang, M. N. Moreno-Garcia, and F. De la Preieta, “Sentiment Analysis Based on Deep Learning: A Comparative Study”, Electronics, Vol. 9, No. 483, pp. 1 – 29, 2020.
[6] P. R. Suri and H. Taneja, “Web Objects Clustering Through Aggregation for Enhanced Search Results”, International Journal of Scientific and Engineering Research, Vol. 2, No. 8, 2011.
[7] E. Cambria, D. Das, S. Bandyopadhyay, and A. Feraco, “A Practical Guide to Sentiment Analysis”, Springer, Berlin, Germany, 2017.
[8] A. Kaur, “A Survey on Sentiment Analysis and Opinion Mining Techniques”, Journal of Emerging Technologies in Web Intelligence, Vol. 5, No. 4, pp. 567 – 571, 2013.
[9] A. Alsaeedi and M. Z. Khan, “A Study on Sentiment Analysis Techniques of Twitter Data”, International Journal of Advanced Computer Science and Application, Vol. 10, No. 2, pp. 361 – 374, 2019.
[10] V. A. Kharde, and S. S. Sonawane, “Sentiment Analysis of Twitter Data: A Survey of Techniques”, International Journal of Computer Applications, Vol. 139, No. 11, pp. 5 - 15, 2016.
[11] M. Ahmad, S. Aftab, S. S. Muhammed, and S. Ahmad, “Machine Learning Techniques for Sentiment Analysis: A Review, Vol. 8, No. 2, 27 – 32, 2017.
[12] B. Kitchenham, and S. Charters (2007) Guidelines for Performing Systematic Literature Reviews in Software Engineering, Technical Report EBSE 2007-001, Keele University and Durham University Joint Report.
[13] A. Schulz, Paulhein, T. D. Thanh, and I. Schweizer, “A Fine-Grained Sentiment Analysis Approach for Detecting Crisis Related Micropost”, proceeding of 10th International ISCRAM Conference, Baden-Baden, Germany, pp. 846 – 851, May 2013.
[14] N. Maladrakis, A. Kazemzadek, A. Potamianos, and S. Narayanan, “SAIL: A Hybrid approach to sentiment analysis”, 2nd Joint Conference on Lexical and Computational Semantics, Vol. 2, pp. 438-442, Atlanta Georgia, June 2013.
[15] S. Wassan, X, Chen, T. Shen, M. Waqar and N. Jhanjhi, “Amazon Product Sentiment Analysis using Machine Language Techniques”, Revista Argentina de Clinica Psicologica, Vol. xxx, No. 1, pp. 693 – 703, 2021.
[16] N. Bahrawi, “Sentiment Analysis using Random Forest Algorithm-Online Social Media Based”, Journal of Information Technology and its Utilization”, vol. 2, no. 2, pp. 29 – 33, 2019.
[17] C. J. Hutto and E. E. Gilbert, “VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text”, Proceeding of the 8th International Conference on Weblogs and SocialMedia (ICWSM-14), 2014.
[18] N. Bahrawi, “Online Realtime Sentiment Analysis Tweets by Utilizing Streaming API Features from Twitter”, Jurnal Penelitian Pos dan Informatika (JPPI), vol. 9, no. 1, pp. 53-62, 2019.
[19] F. Nurhuda, S. W. Sihwi, and A. Doewes, “Analisis Sentimen Masyarakat terhadap Calon Presinden Indonesoa 2014 berdasarkan Opini dari Twitter Menggunakan Metode Naïve Bayes Classifier”, Journal of Teknol Inf ITSmart, vol. 2, no. 2, pp. 35-46, 2016.
[20] G. Xu, Y. Meng, X. Qiu, Z. Yu, and X. Wu, “Sentiment Analysis of Comment Texts based on BiLSTM”, IEEE Access, Vol. xx, pp. 1 – 9, 2017.
[21] Y. Cheng, H. Sun, H. Chen, M. Li, Y. Cai, and J. Huang, “Sentiment Analysis using Multi-head Attention Capsules with Multi-Channel CNN and Bidirectional GRU”, IEEE Access, Vol. 9, pp. 60383 – 60395, 2021.
[22] M. Xuanyuan, L. Xiao, and M. Duan, “Sentiment Classification Algorithm Based on Multi-Modal Social Media Text Information”, IEEE Access, Vol. 9, pp. 33410 – 33418, 2021.
[23] T. Tang, X. Tang, T. Yuang, “Fine-Tuning BERT for Multi-Label Sentiment Analysis in Unbalanced Code-Switching Text”, IEEE Access, Vol. 8, pp. 193248 – 193256, 2020.
[24] U. Naqvl, A. Majid, and S. A. Abbas, “UTSA: Urdu Text Sentiment Analysis using Deep Learning Methods”, IEEE Access, Vol. 9, pp. 114 – 114094, 2021.
[25] J. Singh, G. Singh, and R. Singh, “Optimization od Sentiment Analysis using Machine Learning Classifier”, Human-Centric Computing and Information Science, Vol. 7, No. 32, pp. 1 – 12, 2017.
[26] D. Deng, L. Jing, J. Yu, and S. Sun, “Sparse Self-Attention LSTM for Sentiment Lexicon Construction”, IEEE/ACM Transactions on Audio, Speech and Language Processing, Vol. 27, No. 11, pp. 1777 – 1790, 2019.
[27] M. Bouazizi and T. Ohtsuki, “A Pattern-Based Approach for Multi-Class Sentiment Analysis in Twitter”, IEEE Access, Vol. 5, pp. 20617 – 20639, 2017.
[28] S. Sanagar and D. Gupta, “Unsupervised Genre-Based Multidomain Sentiment Lexicon Learning using Corpus-Generated Polarity Seed Words”, IEEE Access, Vol. 8, pp. 118050 – 118071, 2020.
[29] S. N. Saharudin, K. T. Wei, and K. S. Na, “Machine Learning Techniques for Software Bug Prediction: A Systematic Review”, Journal of Computer Science, Vol. 16, No. 11, pp. 1558 - 1569, 2020.
[30] Y. Feng and Y. Cheng, “Short Text Sentiment Analysis Based on Multi-Channel CNN with Multi-Head Attention Mechanism”, IEEE Access, Vol. 9, pp. 19854 – 19863, 2021.
[31] M. Dong, Y. Li, X. Tang, J. Xu, S. Bi, and Y. Cai, “Variable Convolution and Polling Convolutional Neural Network for Text Sentiment Classification”.
[32] J. Zhou, S. Jin and X. Huang, “ADeCNN: An Improved Model for Aspect-level Sentiment Analysis based on Deformable CNN and Attention”, IEEE Access, Vol. 4, pp. 1 – 11, 2016.
[33] C. R. Aydin and T. Gungor, “Combination of Recursive and Recurrent Neural Networks for Aspect-Based Sentiment Analysis Using Inter-Aspect Relations”, IEEE Access, Vol. 8, pp. 77820 – 77832, 2020.
[34] H. T. Phan, V. C. Tran, N. T. Nguyen and D. Hwang, “Improving the Performance of Sentiment Analysis of Tweets Containing Fuzzy Sentiment Using the Feature Ensemble Model”, IEEE Access, Vol. 4, pp. 1 – 13, 2016.
[35] X. Fu, J. Yang, J. Li, M. Fang and H. Wang, “Lexicon-enhanced LSTM with Attention for General Sentiment Analysis”, IEEE Access, Vol. 10, pp. 1 – 9, 2018.
[36] W. Liu, G. Cao, and J. Yin, “Bi-Level Attention Model for Sentiment Analysis of Short Texts”, IEEE Access, Vol. 7, pp. 119813 – 119822, 2019.
[37] F. Alattar and K. Shaalan, “Using Artificial Intelligence to Understand What Causes Sentiment Changes on Social Media”, IEEE Access, Vol. 9, pp. 61756 – 61767, 2021.
[38] Y. Wang, G. Huang, J. Li, H. Li, Y. Zhou, and H. Jiang, “Refined Global Word Embeddings Based on Sentiment Concept for Sentiment Analysis”, IEEE Access, Vol. 9, pp. 37075 – 37085, 2021.
[39] Z. Hameed and B. Garcia-Zapirain, “Sentiment Classification Using a Single-Layered BiLSTM Model”, IEEE Access, Vol. 8, pp. 73992 – 74001, 2020.
[40] Z. Li, R. Li and G. Jin, “Sentiment Analysis of Danmaku Videos Based on Naïve Bayes and Sentiment Dictionary”, IEEE Access, Vol. 8, 2020.
[41] J. Wu, K. Lu, S. Su and S. Wang, “Chinese Micro-Blog Sentiment Analysis Based on Multiple Sentiment Dictionaries and Semantic Rule Sets”, IEEE Access, Vol. 7, pp. 183924 – 183939, 2019.
[42] H. Jelodar, Y. Wang, R. Orji, and S. Huang, “Deep Sentiment Classification and Topic Discovery on Novel Coronavirus or COVID-19 Online Discussions: NLP Using LSTM Recurrent Neural Network Approach”, IEEE Journal of Biomedical and Health Informatics, Vol. 24, No. 10, pp. 2733-2742, 2020.
[43] L. Wang, J. Niu and S. Yu, “SentiDiff: Combining Textual Information and Sentiment Diffusion Patterns for Twitter Sentiment Analysis”, IEEE Transaction on Knowledge and Data Engineering, Vol. 14, No. 8, pp. 1 – 14, 2018.
[44] J. Zheng and L. Zheng, “A Hybrid Didirectional Recurrent Convolutional Neural Network Attention Based Model for Text Classification”, IEEE Access, Vol. 8, pp. 1-10, 2019.
[45] Z. Hameed, B. Garcia-Zapirain and I. O. Ruiz, “A Computationally efficient BiLSTM based approach for binary sentiment classification”, IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), pp. 1-4. 2019.
[46] B. Venkatesh, S. U. Hegde, Z. A. Zaiba and Y. Nagaraju, “Hybrid CNN-LSTM Model with GloVe Word Vector for Sentiment Analysis on Football Specific Tweets”, 2021 International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), pp. 1 – 8, 2021.
[47] X. Li, H. Zhang, Y. Ouyang, X. Zhang and W. Rong, “A Shadow BERT-CNN Model for Sentiment Analysis on MOOCs Comments”, 2019 IEEE International Conference on Engineering, Technology and Education (TALE), pp. 1 – 6, 2019.
[48] Y. Kamei and E. Shilab, “Defect prediction: Accomplishments and future challenges”, In 2016 IEEE 23rd International conference on software analysis, evolution and reengineering (SANER), Vol. 5, pp. 33-45, 2016.
[49] C. Pan, M. Lu, B. Xu, and H. Gao, “An Improved CNN Model for Within-Project Software Defect Prediction”, Applied Sciences, Vol. 9, No. 10, pp. 21-38, 2019.
[50] S. Hochreiter, and J. Schmidtusber, “Long Short Term Memory”, Neural Comput, Vol. 9, No. 8, pp. 1735 – 1780, 1997.
[51] K. S. Tai, R. Socher and C. D. manning, “Improved Semantic Representations from Tree-Structured Long Short Term Memory Network”, In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, Beijing, China, Vol. I, pp. 1556–1566, May 2015. DOI: 10.3115/v1/P15-1150.
[52] J. Moniz, and D. Krueger, “Nested LSTMs”, In Asian Conference on Machine Learning, Seoul, Korea, pp. 1-5, 2017.