Fine-Grained Sentiment Analysis: Recent Progress

Authors: Jie Liu, Xudong Luo, Pingping Lin, Yifan Fan

Abstract:

Facebook, Twitter, Weibo, and other social media and significant e-commerce sites generate a massive amount of online texts, which can be used to analyse people’s opinions or sentiments for better decision-making. So, sentiment analysis, especially the fine-grained sentiment analysis, is a very active research topic. In this paper, we survey various methods for fine-grained sentiment analysis, including traditional sentiment lexicon-based methods, ma-chine learning-based methods, and deep learning-based methods in aspect/target/attribute-based sentiment analysis tasks. Besides, we discuss their advantages and problems worthy of careful studies in the future.

Keywords: sentiment analysis, fine-grained, machine learning, deep learning

References:

[1] K. Coffman and A. Odlyzko, “Internet growth: Is there a “moore’s law” for data traffic?” in In: Abello J., Pardalos P.M., Resende M.G.C. (eds) Handbook of Massive Data Sets. Massive Computing. Springer, 2002, pp. 47–93.
[2] M.-T. Hu, S.-W. Zhao, L. Zhang, and et al., “CAN: Constrained attention networks for multi-aspect sentiment analysis,” in Proceedings of the 2019 Conference on Empirical Methods in Natural Language Process-ing and the 9th International Joint Conference on Natural Language Processing, vol. 1, 2019, pp. 4600–4609.
[3] G. Diaz, X.-M. Zhang, and V. Ng, “Aspect-based sentiment analysis as fine-grained opinion mining,” in Proceedings of the 12th Language Resources and Evaluation Conference, 2020, pp. 6804–6811.
[4] S. Soumya and K. Pramod, “Fine grained sentiment analysis of malay-alam tweets using lexicon based and machine learning based ap-proaches,” in Proceedings of the 4th Biennial International Conference on Nascent Technologies in Engineering, 2021, pp. 1–6.
[5] Q. Jiang, L. Chen, W. Zhao, and M. Yang, “Toward aspect-level sentiment modification without parallel data,” IEEE Intelligent Systems, vol. 36, no. 1, pp. 75–81, 2021.
[6] C. R. Fink, D. S. Chou, J. J. Kopecky, and A. J. Llorens, “Coarse- and fine-grained sentiment analysis of social media text,” Johns Hopkins Apl Technical Digest, vol. 30, no. 1, pp. 22–30, 2011.
[7] W. Nengsih, M. M. Zein, and N. Hayati, “Coarse-grained sentiment analysis berbasis natural language processing–ulasan hotel,” Jurnal Nasional Teknik Elektro dan Teknologi Informasi, vol. 10, no. 1, pp. 41–48, 2021.
[8] M. I. Prabha and G. U. Srikanth, “Survey of sentiment analysis using deep learning techniques,” in Proceedings of the 1st International Con-ference on Innovations in Information and Communication Technology, 2019, pp. 1–9.
[9] S. Tedmori and A. Awajan, “Sentiment analysis main tasks and appli-cations: A survey,” Journal of Information Processing Systems, vol. 15, no. 3, pp. 500–519, 2019.
[10] Y. Gao and S. Wang, “Application & comparison of several sentiment analysis platforms with open source review dataset,” in Proceedings of the 3rd International Conference on Computer Science and Application Engineering, vol. 78, 2019, pp. 1–78.
[11] P. Lin and X. Luo, “A survey of sentiment analysis based on machine learning,” in Natural Language Processing and Chinese Computing: NLPCC 2020, ser. Lecture Notes in Computer Science, vol. 12430, 2020, pp. 372–387.
[12] ——, “A survey of the applications of sentiment analysis,” International Journal of Computer and Information Engineering, vol. 14, no. 10, pp. 334–346, 2020.
[13] P. Lin, X. Luo, and Y. Fan, “A survey of sentiment analysis based on deep learning,” International Journal of Computer and Information Engineering, vol. 14, no. 12, pp. 473–485, 2020.
[14] X. Guo, W. Yu, and X. Wang, “An overview on fine-grained text sentiment analysis: Survey and challenges,” in Journal of Physics: Conference Series, vol. 1757, no. 1, 2021, p. 012038.
[15] T. N. Prakash and A. Aloysius, “Textual sentiment analysis using lexicon based approaches,” Annals of the Romanian Society for Cell Biology, pp. 9878–9885, 2021.
[16] B. Rojas, “Deep learning for sentiment analysis,” Language & Linguis-tics Compass, vol. 10, no. 12, pp. 205–212, 2016.
[17] C.-Q. Jiang, Y.-B. Guo, and Y. Liu, “Constructing a domain sentiment lexicon based on chinese social media text,” Data Analysis and Knowl-edge Discovery, vol. 3, no. 2, p. 98, 2019.
[18] Y. Gao, P. Su, H. Zhao, M. Qiu, and M. Liu, “Research on sentiment dictionary based on sentiment analysis in news domain,” in Proceedings of 2021 IEEE International Conference on Big Data Security on Cloud, High Performance and Smart Computing, and Intelligent Data and Security, 2021, pp. 117–122.
[19] J. Rouces, L. Borin, and N. Tahmasebi, “Creating an annotated corpus for aspect-based sentiment analysis in swedish,” in Proceedings of the 5th Conference on the Digital Humanities in the Nordic Countries, 2020, pp. 318–324.
[20] F. Bravo-Marquez, E. Frank, and B. Pfahringer, “Positive, negative, or neutral: Learning an expanded opinion lexicon from emoticon-annotated tweets,” in Proceedings of the 24th International Joint Conference on Artificial Intelligence, 2015, pp. 1229–1235.
[21] M. Du, X. Li, and L. Luo, “A training-optimization-based method for constructing domain-specific sentiment lexicon,” Complexity, vol. 2021, 2021.
[22] H.-T. Liu, J.-C. Zhu, X.-Y. Liu, G.-G. Li, Q.-M. Qian, N. Su, and N. Wang, “Expansion of sentiment lexicon based on label propagation,” in Proceedings of the 1 5th International Conference on Semantics, Knowledge and Grids, 2019, pp. 145–152.
[23] Z. Ren, G. Zeng, L. Chen, Q. Zhang, C. Zhang, and D. Pan, “A lexicon-enhanced attention network for aspect-level sentiment analysis,” IEEE Access, vol. 8, pp. 93 464–93 471, 2020.
[24] L. Yang, Y. Li, J. Wang, and R. S. Sherratt, “Sentiment analysis for e-commerce product reviews in chinese based on sentiment lexicon and deep learning,” IEEE Access, vol. 8, pp. 23 522–23 530, 2020.
[25] O. M. Beigi and M. H. Moattar, “Automatic construction of domain-specific sentiment lexicon for unsupervised domain adaptation and sen-timent classification,” Knowledge-Based Systems, vol. 213, p. 106423, 2021.
[26] S. Li, W. Shi, J. Wang, and H. Zhou, “A deep learning-based approach to constructing a domain sentiment lexicon: a case study in financial distress prediction,” Information Processing & Management, vol. 58, no. 5, p. 102673, 2021.
[27] R. Li, Z. Lin, P. Fu, and et al., “Emomix: Building an emotion lexicon for compound emotion analysis,” in Proceedings of the 2019 International Conference on Computational Science, 2019, pp. 353–368.
[28] C.-H. Wang, K.-C. Fan, C.-J. Wang, and M. Tsai, “Ugsd: User generated sentiment dictionaries from online customer reviews,” in Proceedings of the 33rd AAAI Conference on Artificial Intelligence, 2019, pp. 313–320.
[29] Turney and D. Peter, “Thumbs up or thumbs down? semantic orientation applied to unsupervised classification of reviews,” In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 417–424, 2002.
[30] R. Y. Purba, “Analisis brand awareness dan sentiment analysis dengan media sosial twitter pada produk laptop di indonesia,” Ph.D. dissertation, Institut Teknologi Sepuluh Nopember, 2021.
[31] B. Han, M. Han, and J. S. He, “Fine-grained sentiment analysis for customer review,” https:// digitalcommons.kennesaw.edu/ cs etd/ 47, 2021.
[32] N. Liu and B. Shen, “Aspect-based sentiment analysis with gated alter-nate neural network,” Knowledge-Based Systems, vol. 188, p. 105010, 2020.
[33] H.-Y. Peng, L. Xu, L.-D. Bing, F. Huang, W. Lu, and L. Si, “Knowing what, how and why: A near complete solution for aspect-based sentiment analysis,” in Proceedings of the 34th AAAI Conference on Artificial Intelligence, 2020, pp. 8600–8607.
[34] K. Zhang, H.-F. Zhang, Q. Liu, H.-K. Zhao, H.-S. Zhu, and E.-H. Chen, “Interactive attention transfer network for cross-domain sentiment classification,” in Proceedings of the 33rd AAAI Conference on Artificial Intelligence, 2019, pp. 5773–5780.
[35] L. Zhao, Y. Liu, M. Zhang, T. Guo, and L. Chen, “Modeling label-wise syntax for fine-grained sentiment analysis of reviews via memory-based neural model,” Information Processing & Management, vol. 58, no. 5, p. 102641, 2021.
[36] Y. Tian, G. Chen, and Y. Song, “Aspect-based sentiment analysis with type-aware graph convolutional networks and layer ensemble,” in Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2021, pp. 2910–2922.
[37] Z. Lei, Y. Yang, M. Yang, W. Zhao, J. Guo, and Y. Liu, “A human-like semantic cognition network for aspect-level sentiment classification,” in Proceedings of the 33rd AAAI Conference on Artificial Intelligence, 2019, p. 6650–6657.
[38] Z. Li, Y. Wei, Y. Zhang, X. Zhang, and X. Li, “Exploiting coarse-to-fine task transfer for aspect-level sentiment classification,” in Proceedings of the 33rd AAAI Conference on Artificial Intelligence, 2019, p. 4253–4260.
[39] X.-Y. Ran, Y.-Y. Pan, W. Sun, and C.-J. Wang, “Learn to select via hierarchical gate mechanism for aspect-based sentiment analysis,” in Proceedings of the 28th International Joint Conference on Artificial Intelligence, 2019, pp. 5160–5167.
[40] C. Sindhu and G. Vadivu, “Fine grained sentiment polarity classification using augmented knowledge sequence-attention mechanism,” Micropro-cessors and Microsystems, vol. 81, p. 103365, 2021.
[41] N. Zhao, H. Gao, X. Wen, and H. Li, “Combination of convolutional neural network and gated recurrent unit for aspect-based sentiment analysis,” IEEE Access, vol. 9, pp. 15 561–15 569, 2021.
[42] Z. Cao, Y. Zhou, A. Yang, and S. Peng, “Deep transfer learning mecha-nism for fine-grained cross-domain sentiment classification,” Connection Science, pp. 1–18, 2021.
[43] H. Devamanyu, P. Soujanya, V. Prateek, K. Gangeshwar, C. Erik, and Z. Roger, “Modeling inter-aspect dependencies for aspect-based sentiment analysis,” in Proceedings of the North American Chapter of the Association for Computational Linguistic, 2018, p. 266–270.
[44] A. Ghosh, P. Kotekal, and G. Chakraborty, “Attribute-based sentiment analysis in marketing: Application and strategic implications,” Model Assisted Statistics and Applications, vol. 13, no. 4, pp. 311–318, 2018.
[45] B. Bansal and S. Srivastava, “Hybrid attribute based sentiment classifica-tion of online reviews for consumer intelligence,” Applied Intelligence, vol. 49, no. 1, pp. 137–149, 2019.
[46] ——, “Context-sensitive and attribute-based sentiment classification of online consumer-generated content,” Kybernetes, 2019.
[47] M. Pontiki, D. Galanis, J. Pavlopoulos, and et al., “Semeval-2014 task 4: Aspect based sentiment analysis,” in Proceedings of the 8th International Workshop on Semantic Evaluation, 2014, pp. 27–35.
[48] A. Pablos, M. Cuadros, and G. Rigau, “V3: Unsupervised aspect based sentiment analysis for semeval2015 task 12,” in Proceedings of the 9th International Workshop on Semantic Evaluation, 2015, pp. 714–718.
[49] M. Pontiki, D. Galanis, H. Papageorgiou, and et al., “SemEval-2016 Task 5: Aspect based sentiment analysis,” in Proceedings of the 10th International Workshop on Semantic Evaluation, 2016, pp. 19–30.
[50] K. Cortis, A. Freitas, T. Daudert, and et al., “SemEval-2017 Task 5: Fine-grained sentiment analysis on financial microblogs and news,” in Proceedings of the 11th International Workshop on Semantic Evaluation, 2017, pp. 519–535.
[51] C.-H. Wu, F.-Z. Wu, J.-X. Liu, and et al., “THU NGN at semEval-2018 task 1: Fine-grained tweet sentiment intensity analysis with attention CNN-LSTM,” in Proceedings of the 12th International Workshop on Semantic Evaluation, 2018, pp. 186–192.
[52] J. Anderson, “Sentim at semeval-2019 task 3: Convolutional neural networks for sentiment in conversations,” in Proceedings of the 13th International Workshop on Semantic Evaluation, 2019, pp. 302–306.
[53] X. Li, L.-D. Bing, W.-X. Zhang, and W. Lam, “Exploiting bert for end-to-end aspect-based sentiment analysis,” in Proceedings of the 5th Workshop on Noisy User-generated Text, 2019, pp. 34–41.
[54] C. Wu, Q. Xiong, H. Yi, Y. Yu, Q. Zhu, M. Gao, and J. Chen, “Multiple-element joint detection for aspect-based sentiment analysis,” Knowledge-Based Systems, vol. 223, p. 107073, 2021.
[55] A. Kumar, P. Gupta, R. Balan, L. B. M. Neti, and A. Malapati, “Bert based semi-supervised hybrid approach for aspect and sentiment classification,” Neural Processing Letters, pp. 1–18, 2021.
[56] M. Shams, N. Khoshavi, and A. Baraani-Dastjerdi, “LISA: language-independent method for aspect-based sentiment analysis,” IEEE Access, vol. 8, pp. 31 034–31 044, 2020.
[57] J. Tao and X. Fang, “Toward multi-label sentiment analysis: A transfer learning based approach,” Journal of Big Data, vol. 7, no. 1, pp. 1–26, 2020.
[58] S. Tu and B. Yang, “Research on sentiment classification of micro-blog short text based on topic clustering,” in Journal of Physics: Conference Series, vol. 1827, no. 1. IOP Publishing, 2021, p. 012160.
[59] R. K. Yadav, L. Jiao, O.-C. Granmo, and M. Goodwin, “Human-level interpretable learning for aspect-based sentiment analysis,” in Proceedings of the 35th AAAI Conference on Artificial Intelligence. AAAI, 2021.
[60] J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” in Pro-ceedings of the 2019 North American Chapter of the Association for Computational Linguistics, 2019, p. 4171–4186.
[61] V. M. Patel, R. Gopalan, and R.-N. Li, “Visual domain adaptation: An overview of recent advances,” Umiacs.umd.edu, no. 3, pp. 53–59, 2015.
[62] K. Feng and T. Chaspari, “A review of generalizable transfer learning in automatic emotion recognition,” Frontiers in Computer Science, vol. 2, p. 9, 2020.
[63] M. Diaz, I. Johnson, A. Lazar, and et al., “Addressing age-related bias in sentiment analysis,” in Proceedings of the 28th International Joint Conference on Artificial Intelligence, 2019, pp. 6146–6150.