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Multi-way matching based fine-grained sentiment analysis for user reviews

  • S.I. : ATCI 2019
  • Published:
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Abstract

While sentiment analysis has been widely used in public opinion to explore tendency of users for a target product from large online review data, less work focus on aspect-level or fine-grained sentiment analysis in which the polarity of not only the aspect of a target object but also the attribute of that given aspect should be determinated. Recent work regards aspect-level sentiment analysis as two separate tasks, i.e., aspect classification and sentiment analysis, and this pipeline method leads to error propagation. To address this issue, this paper proposes an improved multi-way matching deep neural network model for fine-grained sentiment analysis, which jointly models the two tasks in one phase and improves current attention by directly capturing past attention in the multi-round alignment architecture, so as to prevent error propagation and attention deficiency problems. Experimental results on fine-grained sentiment analysis data sets of catering industry indicate that the F1 score of this model in actual test set reaches 0.7302 and EM score 87.1973, which are higher than baseline DocRNN model by 3.8% and 0.88% in F1 and EM, and are higher than SVM by 15.4% and 25.6%, which verified that our model could effectively predict fine-grained sentiment and have better generalization performance.

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Notes

  1. https://github.com/MobTgZhang/NlpSentimentAnalysis/tree/master/CoreCode.

  2. https://fasttext.cc/docs/en/crawl-vectors.html.

References

  1. Pang B, Lee L (2008) Opinion mining and sentiment analysis. Found Trends Inf Retr 2(1–2):1–135

    Article  Google Scholar 

  2. Song K, Gao W, Feng S et al (2017) Recommendation vs Sentiment Analysis: A Text-Driven Latent Factor Model for Rating Prediction with Cold-Start Awareness. In: The 26th international joint conference on artificial intelligence (IJCAI 2017)

  3. Sirisha Devi J, Nandyala Siva Prasad, Vijaya Bhaskar Reddy P (2019) A novel approach for sentiment analysis of public posts. Innov Comput Sci Eng 32:161

    Article  Google Scholar 

  4. Ahmad DK (2007) Sentiment polarity identification in financial news: a cohesion-based approach. In: Proceedings of the 45th annual meeting of the association of computational linguistics (ACL2007), Prague, pp 984–991

  5. Elalfy D, Gad W, Ismail R (2015) Predicting best answer in community questions based on content and sentiment analysis. In: 2015 IEEE seventh international conference on intelligent computing and information systems (ICICIS). IEEE

  6. Liu B (2012) Sentiment analysis and opinion mining. Synth Lect Hum Lang Technol 5(1):1–167

    Article  Google Scholar 

  7. Ku LW, Liang YT, Chen HH (2006) Opinion extraction, summarization and tracking in news and blog corpora. In: Proceedings of AAAI’2006, pp 100–107

  8. Gyamfi Y, Wiebe J, Mihalcea R, et al (2009) Integrating Knowledge for Subjectivity Sense Labeling. In: Human language technologies: the conference of the North American chapter of the association for computational linguistics. Association for computational linguistics

  9. Esuli A, Sebastiani F (2007) PageRanking WordNet Synsets: an application to opinion mining. In: Proceeding of 2007 ACL, pp 424–431

  10. Dong ZD (2010) HowNet knowledge database. http://www.keenage.com

  11. Liu W, Zhu Y, Li C et al (2009) Research on building Chinese basic Sentiment lexicon. J Comput Appl (in Chinese) 29(10):2875–2877

    Google Scholar 

  12. Xu L, Lin H, Pan Y et al (2008) Constructing the affective lexicon ontology. J China Soc Sci Tech Inf (in Chinese) 27(2):180–185

    Google Scholar 

  13. Wiebe J, Breck E, Buckley C et al (2002) NRRC summer workshop on multi-perspective question answering

  14. Quan CQ, Ren FJ (2009) Construction of a blog emotion corpus for chinese emotional expression analysis. In: 2009 proceedings of EMNLP, 2009, pp 1446–1454

  15. Liao J, Li Y, Wang S (2016) The constitution of a fine-grained opinion annotated corpus on weibo. Lect Notes Artif Intel 2016:227–240

    Google Scholar 

  16. Wagner J, Arora P, Cortes S, Barman U, Bogdanova D, Foster J, Tounsi L (2014) Dcu: aspect-based polarity classification for semeval task 4. In: Proceedings of the 8th international workshop on semantic evaluation (SemEval 2014), pp 223–229

  17. Liao J, Wang S, Li D et al (2017) FREERL: fusion relation embedded representation learning framework for aspect extraction. Knowl Based Syst 135:9–17

    Article  Google Scholar 

  18. Septiandri AA, Sutiono AP (2019) Aspect and opinion term extraction for aspect based sentiment analysis of hotel reviews using transfer learning. arXiv:1909.11879 [Cs]. Accessed 7 Nov 2019

  19. Hu M, Zhao S, Guo H, Cheng R, Su Z (2019) Learning to detect opinion snippet for aspect-based sentiment analysis. https://arxiv.org/abs/1909.11297v1. Accessed 7 Nov 2019

  20. Tang D, Qin B, Feng X, Liu T (2015) Target-dependent sentiment classification with long short term memory. ArXiv preprint 2015, arXiv:1512.01100

  21. Huang B, Carley KM (2019) Parameterized convolutional neural networks for aspect level sentiment classification. https://arxiv.org/abs/1909.06276v1. Accessed 7 Nov 2019

  22. Tang D, Qin B, Liu T (2016) Aspect level sentiment classification with deep memory network. arXiv e-prints 2016, arXiv:1605.08900

  23. He, R, Lee WS, Ng HT, Dahlmeier D (2019) An interactive multi-task learning network for end-to-end aspect-based sentiment analysis. arXiv:1906.06906 [cs]. Accessed 7 Nov 2019

  24. Li Y, Yin C, Wei T et al. (2019) A joint model for aspect-category sentiment analysis with contextualized aspect embedding. https://arxiv.org/abs/1908.11017v1. Accessed 7 Nov 2019

  25. Peng H, Xu L, Bing L et al (2019) Knowing what, how and why: a near complete solution for aspect-based sentiment analysis. http://arxiv.org/abs/1911.01616 [Cs]. Accessed 7 Nov 2019

  26. Dey L, Chakraborty S, Biswas A (2016) Sentiment analysis of review datasets using Naïve Bayes‘ and K–NN classifier. Int J Inf Eng Electron Bus 8(4):54–62

    Google Scholar 

  27. Turney PD (2002) Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In: Proceedings of annual meeting of the association for computational linguistics, pp 417–424

  28. Pang B, Lee L, Vaithyanathan S (2002) sentiment classification using machine learning techniques. In: Proceedings of the 2002 conference on empirical methods in natural language processing, ACL, pp 79–86

  29. Tan C, Wei F, Wang W, Lv W, Zhou M (2018) Multiway attention networks for modeling sentence Pairs. In: Proceedings of the 27th international joint conference on artificial intelligence (IJCAI), pp 4411–4417

  30. Lin Z, Feng M, Santos CND et al (2017) A structured self-attentive sentence embedding. In: Proceedings of the 5th international conference on learning representations (ICLR2017)

  31. Wang W, Yang N, Wei F, Chang B, Zhou M (2017) Gated self-matching networks for reading comprehension and question answering. In: Association for computational linguistics (ACL)

  32. Chen D, Fisch A, Weston J, Bordes A (2017) Reading Wikipedia to answer open-domain questions. arXiv:1704.00051 [cs]

  33. Hu M, Peng Y, Huang Z et al (2018) Reinforced mnemonic reader for machine reading comprehension. In: Proceedings of the 27th international joint conference on artificial intelligence (IJCAI)

  34. Tai KS, Socher R, Manning CD (2015) Improved semantic representations from tree-structured long short-term memory networks. In: Proceedings of the 53th annual meeting of the association for computational linguistics, ACL2015

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Acknowledgements

This study was supported by the National Natural Science Foundation of China (Grant No. 61502288, 61632011, 61573231, 61403238, 61673248), the National Natural Science Foundation of Shanxi Provincial (Grant No. 201701D221101, 201901D111032), and the National High Technology Research and Development Program of China (Grant No. 2018YFB1005103). The authors would also like to thank the anonymous reviewers for their invaluable comments.

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Authors and Affiliations

  1. School of Computer and Information Technology, Shanxi University, Taiyuan, Shanxi, China

    Xin Guo, Geng Zhang, Suge Wang & Qian Chen

  2. Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan, Shanxi, China

    Suge Wang & Qian Chen

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  1. Xin Guo
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  2. Geng Zhang
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  3. Suge Wang
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  4. Qian Chen
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Correspondence to Qian Chen.

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Guo, X., Zhang, G., Wang, S. et al. Multi-way matching based fine-grained sentiment analysis for user reviews. Neural Comput & Applic 32, 5409–5423 (2020). https://doi.org/10.1007/s00521-019-04686-9

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