A Review and Comparative Analysis of Sentiment Analysis Techniques

Shaha T. Al-Otaibi, Amal A. Al-Rasheed

Abstract


Social networking platforms have become a major source of information, which covers a wide range of topics and has gained a large volume of usage by people around the world. Platforms such as Twitter, Facebook, Instagram, and LinkedIn have attracted huge numbers of users who create public profiles and communicate with other users in the network. They exchange videos, posts, and comments. Social networking requires appropriate techniques to analyze the huge amount of complex, and frequently updated data generated. Sentiment Analysis is one such method of handling this vast volume of data and extracting useful knowledge from it. Social networking contents are analyzed using different techniques to gain insight from this data and use it in decision-making processes. The aim of this work is to study the sentiment analysis concept and present state-of-the-art techniques as well as provide a comparative study of these techniques.



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References


J. Guerreiro and P. Rita, "How to predict explicit recommendations in online reviews using text mining and sentiment analysis," Journal of Hospitality and Tourism Management, vol. 43, p. 269–272, 2020.

A. J. Sanur Sharma, "Role of sentiment analysis in social media security and analytics," Wiley Interdisciplinary Reviews- Data Mining and Knowledge Discovery, vol. 10, no. 51,, p. 1366, 2020.

S. Al-Otaibi, A. Alnassar, A. Alshahrani, A. Al-Mubarak, S. Albugami and N. Almutiri, "Customer satisfaction measurement using sentiment analysis," International Journal of Advanced Computer Science and Applications, vol. 9, no. 2, pp. 106-117, 2018.

D. M. E.-D. M. Hussein, "A survey on sentiment analysis challenges," King Saud University - Engineering Sciences, vol. 30, no. 4, p. 330–338, 2018.

M. Reformat and K. S. Golmohammadi, "Rule- and OWA-based semantic similarity for user profiling," International Journal of Fuzzy Systems, vol. 12, no. 2, pp. 87-102, 2010.

W. Fan and M. D. Gordon, "The power of social media analytics," Communications of the ACM, vol. 57, no. 6, p. 74–81, 2014.

O. Chong, S. Sheila and A. Soliman, "Social media analysis framework: the case of Twitter and super bowl ads," Journal of Information Technology Management, vol. XXVI, no. 1, pp. 1-18, 2015.

E. Younis, "Sentiment analysis and text mining for social media microblogs using open source tools: an empirical study," International Journal of Computer Applications, vol. 112, no. 5, pp. 44-48, February 2015.

F. A. Pozzi, E. Fersini, E. Messina and B. Liu, Sentiment Analysis in Social Networks, 1st ed., Henderson, NV, USA: Morgan Kaufmann, 2017.

G. Beigi, X. Hu, R. Maciejewski and H. Liu, "An overview of sentiment analysis in social media and its applications in disaster relief," Sentiment analysis and ontology engineering, vol. 639, p. 313–340, 2016.

R. Tejwani, "Sentiment Analysis: A Survey," ArXiv, pp. 1-3, 2014.

B. Pang, L. Lee and S. Vaithyanathan, "Thumbs up? Sentiment Classification using Machine Learning Techniques," in proc. of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2002), Philadelphia, PA, USA, 2002.

MohammadAl-Smadi, M. Al-Ayyoub, Y. Jararweh and O. Qawasmeh, "Enhancing aspect-based sentment analysis of Arabic hotels’ reviews using morphological, syntactic and semantic features," Information Processing and Management, vol. 56, no. 2, pp. 308-319, 2019.

N. Zainuddin and A. Selamat, "Sentiment analysis using support vector machine," in proc. International Conference on Computer, Communications, and Control Technology (I4CT), Kuching, Malaysia, 2014.

L. Vega and A. Mendez-Vazquez, "Dynamic neural networks for text classification," in proc. International Conference on Computational Intelligence and Applications (ICCIA), Leuven, Belgium, 2016.

D. Sharma, M. Sabharwal, V. Goyal and M., "Sentiment Analysis Techniques for Social Media Data: A Review," in proc. First International Conference on Sustainable Technologies for Computational Intelligence, Singapore, pp. 75-90, 2020.

M. Unnisa, A. Ameen and S. Raziuddin, "Opinion mining on Twitter data using unsupervised learning technique," International Journal of Computer Applications, vol. 148, no. 12, pp. 12-19, 2016.

L. R. C. Pessutto, D. S. Vargas and V. P. Moreira, "Multilingual aspect clustering for sentiment analysis," Knowledge-Based Systems, vol. 192, no. 9, 2020.

N. C. Dang, M. N. Moreno-García and F. D. P. la, "Sentiment analysis based on deep learning: a comparative study," Electronics, vol. 9, no. 3, pp. 483-512, 2020.

C. C. Aggarwal, Neural Networks and Deep Learning, Berlin, Germany: Springer, 2018.

O. Araque, I. Corcuera-Platas, F. J. anchez-Rada and C. A. Iglesias, "Enhancing deep learning sentiment analysis with ensemble techniques in social applications," Expert System with Applications, vol. 77, p. 236–246, 2017.

A. M. Ramadhani and H. S. Goo, "Twitter sentiment analysis using deep learning methods," in proc. 7th International Annual Engineering Seminar (InAES), Yogyakarta, Indonesia, 2017.

D. Britz, "Recurrent neural networks tutorial, part 1–introduction to RNNs.," WILDML Artificial Intelligence, Deep Learning, and NLP, 17 September 2020. [Online]. Available: wildml.com.

G. Preethi, P. V. Krishna, M. S. Obaidat, V. Saritha and S. Yenduri, "Application of deep learning to sentiment analysis for recommender system on cloud," in proc. 2017 International Conference on Computer, Information and Telecommunication Systems (CITS), Dalian, China, 2017.

P. Patil and P. Yalagi, "Sentiment analysis levels and techniques: a survey," International Journal of Innovations in Engineering and Technology (IJIET), vol. 6, no. 4, pp. 523-528, 2016.

Y. Li, Q. Jin, M. Zuo, H. Li, X. Yang, Q. Zhang and X. Liu, "Multi-neural network- based sentiment analysis of food reviews based on character and word embeddings," International Journal of Electrical Engineering and Education, pp. 1-12, 2020.

S. Park and K. Y, "Building thesaurus lexicon using dictionary-based approach for sentiment classification," in proc. IEEE 14th International Conference on Software Engineering research, Management and Applications (SERA), Towson, MD, USA, 2016.

H. Saif, Y. He and M. Fernandez, "Contextual semantics for sentiment analysis of twitter," Information Processing and Management, vol. 52, no. 1, p. 5–19, 2016.

A. Esuli and F. Sebastiani, "Determining the semantic orientation of terms through gloss classification," in proc. of 4th ACM International Conference on Information and Knowledge Management, Bremen, Germany, 2005.

M. R. Huq, A. Ali and A. Rahman, "Sentiment analysis on Twitter data using KNN and SVM," (IJACSA) International Journal of Advanced Computer Science and Applications, vol. 8, no. 6, pp. 19-25, 2017.

C. Wu and L. Shen, "A new method of using contextual information to infer the semantic orientations of context dependent opinions," in proc. International Conference on Artificial Intelligence and Computational Intelligence, Shanghai, China, pp. 274-278, 2009.

T.-C. Peng and C.-C. Shih, "An unsupervised snippet-based sentiment classification method for chinese unknown phrases without using reference word pairs," in proc. IEEE/WIC/ACM International Conference on Web Intelligence and intelligent Agent Technology, Toronto, Canada, 2010.

G. Li and F. Liu, "A Clustering-based approach on sentiment analysis," in proc. IEEE International Conference on Intelligent Systems and Knowledge Engineering, ISKE, Hangzhou, China, pp. 31-337, 2010.

L. Z. Chaovalit, "Movie review mining: a comparison between supervised and unsupervised classification approaches," in proc. of the 38th Hawaii International Conference on System Sciences, Hawaii, USA, pp. 112, 2005.

N. A. Abdulla, N. Ahmed, M. Shehab and M. Al-Ayyoub, "Arabic sentiment analysis: Lexicon-based and corpus-based and corpus-based," in proc. IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT), ., Amman, Jordan, 2013.

R. Xia, F. X. F, J. Yu and E. Y. Cambria, "Polarity shift detection, elimination and ensemble a three-stage model for document-level sentiment analysis," Information Processing and Management, vol. 52, no. 1, p. 36–45, 2016.

A. I. Hajar Rehioui, "New clustering algorithms for Twitter sentiment analysis," IEEE Systems Journal, vol. 14, no. 1, pp. 530-537, 2020.

Z.-P. Fan, G.-M. Li and Y. Liu, "Processes and methods of information fusion for ranking products based on online reviews: an overview," Information Fusion, vol. 60, pp. 87-97, 2020.

M. L. B. Estrada, R. Z. Cabada, R. O. Bustillos and M. Graff, "Opinion mining and emotion recognition applied to learning environments," Expert Systems With Applications, Vols. 150, 113265, 2020.

Y. Shanshan and L. Xiaofang, "Machine learning based customer sentiment analysis for recommending shoppers, shops based on customers’ review," Complex and Intelligent Systems, vol. 6, no. 5, pp. 1-14, 2020.

A. Onan, "Sentiment analysis on product reviews based on weighted word embeddings and deep neural networks," Concurrency Computation- Practice and Experience, no. Special Issue, pp. 1-12, 2020.

K. Shuang, Q. Yang, J. Loo, R. Li and M. Gu, "Feature distillation network for aspect-based sentiment analysis," Information Fusion, vol. 61, pp. 13-23, 2020.

P. J. Khiabani, M. E. Basiri and H. Rastegari, "An improved evidence-based aggregation method for sentiment analysis," Journal of Information Science, vol. 46, no. 3, p. 340–360, 2020.

C. Sun, L. Huang and X. Qiu, "Utilizing BERT for aspect-based sentiment analysis via constructing auxiliary sentence," in proc. NAACL HLT- Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Turku, Finland, 2019.

C.-F. Tsai, K. Chen, Y.-H. Hu and W.-K. Chen, "Improving text summarization of online hotel reviews with review helpfulness and sentiment," Tourism Management, Vols. 80, 104122, 2020.

N. Liu and B. Shen, "ReMemNN: A novel memory neural network for powerful interaction in aspect-based sentiment analysis," Neurocomputing, vol. 395, pp. 66-77, 2020.

M. Wang and G. Hu, "A novel method for Twitter sentiment analysis based on attentional-graph neural network," Information, vol. 11, no. 2 , p. 92, 2020.

C. Song, X.-K. Wang, P.-f. Cheng, J.-q. Wang and L. Li, "SACPC: A framework based on probabilistic linguistic terms for short text sentiment analysis," Knowledge-Based Systems, Vols. 194, 105572, 2020.

M. Emadi and M. Rahgozar, "Twitter sentiment analysis using fuzzy integral classifier fusion," Journal of Information Science, vol. 46, no. 2, p. 226–242, 2020.

T. P. Sahu and S. Khandekar, "A machine learning-based Lexicon approach for sentiment analysis," International Journal of Technology and Human Interaction, vol. 16, no. 2, pp. 8-22, 2020.

L. Rafael, C. Pessutto, D. S. Vargas and V. P. Moreira, "Multilingual aspect clustering for sentiment analysis," Knowledge-Based Systems, Vols. 192, 105339, 2020.

G. A. Ruz, P. A. Henríquez and A. Mascareño, "Sentiment analysis of Twitter data during critical events through Bayesian networks classifiers," Future Generation Computer Systems, vol. 106, p. 92–104, 2020.

M. Asif, A. Ishtiaq, H. Ahmad, H. Aljuaid and J. Shah, "Sentiment analysis of extremism in social media from textual information," Telematics and Informatics, Vols. 48, 101345, 2020.

B. Liu, S. Tang, X. Sun, Q. Chen, J. Cao, J. Luo and S. Zhao, "Context-aware social media user sentiment analysis," Tsinghua Science and Technology, vol. 25, no. 4, p. 528–541, 2020.




DOI: https://doi.org/10.31449/inf.v46i6.3991

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