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A Discriminative Approach to Sentiment Classification

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Abstract

Due to the explosive growth of user-generated contents, understanding opinions (such as reviews on products) generated by Internet users is important for optimizing business decision. To achieve such understanding, this paper investigates a discriminative approach to classifying opinions according to sentiments. The discriminative approach builds a model with the prior knowledge of the categorization information in order to extract meaningful features from the unstructured texts. The prior knowledge includes ratio factors to reinforce terms’ sentiment polarity by using TF-IDF, short for term frequency-inverse document frequency. Experimental results with four datasets show the proposed approach is very competitive, compared with some of the previous works.

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References

  1. Li G, Liu F (2012) Application of a clustering method on sentiment analysis. J Inf Sci 38(2):127–139

    Google Scholar 

  2. Jun Y, Yang X, Gao F, Tao D (2016) Deep multimodal distance metric learning using click constraints for image ranking. IEEE Trans Cybern 47(12):4014–4024

    Google Scholar 

  3. Jun Y, Tao D, Wang M, Rui Y (2014) Learning to rank using user clicks and visual features for image retrieval. IEEE Trans Cybern 45(4):767–779

    Google Scholar 

  4. Jun Y, Rui Y, Tao D (2014) Click prediction for web image reranking using multimodal sparse coding. IEEE Trans Image Process 23(5):2019–2032

    Google Scholar 

  5. Jun Y, Chaoyang Z, Jian Z, Qingming H, Dacheng T (2019) Spatial pyramid-enhanced NetVLAD with weighted triplet loss for place recognition. IEEE Trans Neural Netw Learn Syst. https://doi.org/10.1109/tnnls.2019.2908982

  6. Jun Y, Kuang Z, Zhang B, Zhang W, Lin D, Fan J (2018) Leveraging content sensitiveness and user trustworthiness to recommend fine-grained privacy settings for social image sharing. IEEE Trans Inf Forensics Secur 13(5):1317–1332

    Google Scholar 

  7. Tao D, Hong C, Yu J, Wan J, Wang M (2015) Multimodal deep autoencoder for human pose recovery. IEEE Trans Image Process Publ IEEE Signal Process Soc 24(12):5659–5670

    Google Scholar 

  8. Hong C, Jun Y, Tao D, Meng W (2015) Image-based three-dimensional human pose recovery by multiview locality-sensitive sparse retrieval. IEEE Trans Ind Electron 62(6):3742–3751

    Google Scholar 

  9. Gerard S (1968) Automatic information organization and retrieval. McGraw Hill Text, New York

    Google Scholar 

  10. Justin M, Tim F (2009) Delta TFIDF: an improved feature space for sentiment analysis. In: Proceedings of the 3rd AAAI international conference on weblogs and social media, pp 258–261

  11. Lan M, Tan CL, Su J, Lu Y (2009) Supervised and traditional term weighting methods for automatic text categorization. IEEE Trans Pattern Anal Mach Intell 31(4):721–735

    Google Scholar 

  12. Thelwall M, Buckley K, Paltoglou G, Cai D, Arvid K (2010) Sentiment strength detection in short informal text. J Am Soc Inf Sci Technol 61(12):2544–2558

    Google Scholar 

  13. Nguyen TT, Chang K, Hui SC (2011) Supervised term weighting for sentiment analysis. In: IEEE international conference on intelligence and security informatics, pp 89–94

  14. Pang B, Lee L, Vaithyanathan S (2002) Thumbs up?: sentiment classification using machine learning techniques. In: Proceedings of the ACL-02 conference on empirical methods in natural language processing. Association for Computational Linguistics, vol 10, pp 79–86

  15. Pang B, Lee L (2004) A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. In: Proceedings of the 42nd annual meeting on association for computational linguistics. Association for Computational Linguistics, p 271

  16. Kim S-M, Pantel P, Chklovski T, Pennacchiotti M (2006) Automatically assessing review helpfulness. In: Proceedings of the 2006 conference on empirical methods in natural language processing. Association for Computational Linguistics, pp 423–430

  17. Zhai Z, Xu H, Li J, Jia P (2009) Sentiment classification for Chinese reviews based on key substring features. In: 2009 International conference on natural language processing and knowledge engineering, pp 1–8

  18. Paltoglou G, Thelwall M (2010) A study of information retrieval weighting schemes for sentiment analysis. In: Proceedings of the 48th annual meeting of the association for computational linguistics. Association for Computational Linguistics, pp 1386–1395

  19. Hu M, Liu B (2004) Mining and summarizing customer reviews. In: Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining, pp 168–177. ACMD

  20. Oliveira N, Cortez P, Areal N (2013) On the predictability of stock market behavior using stocktwits sentiment and posting volume. In: Portuguese conference on artificial intelligence, pp 355–365. Springer, Berlin

  21. Di Fatta G, Reade JJ, Jaworska S, Nanda A (2015) Big social data and political sentiment: the tweet stream during the UK general election 2015 campaign. In: 2015 IEEE international conference on smart city/socialcom/sustaincom (smartcity), pp 293–298

  22. Scheible C, Schütze H (2012) Bootstrapping sentiment labels for unannotated documents with polarity pagerank. In: LREC, pp 1230–1234

  23. Jones KS (1972) A statistical interpretation of term specificity and its application in retrieval. J Doc 28(1):11–21

    Google Scholar 

  24. Wang S, Manning CD (2013) Fast dropout training. In: ICML (2), pp 118–126

  25. Maas AL, Daly RE, Pham PT, Huang D, Ng AY, Potts C (2011) Learning word vectors for sentiment analysis. In: Proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies. Association for Computational Linguistics, vol 1, pp 142–150

  26. Wiebe J, Wilson T, Cardie C (2005) Annotating expressions of opinions and emotions in language. Lang Resour Eval 39(2–3):165–210

    Google Scholar 

  27. Wang S, Manning CD (2012) Baselines and bigrams: simple, good sentiment and topic classification. In: Proceedings of the 50th annual meeting of the association for computational linguistics: short papers. Association for Computational Linguistics, vol 2, pp 90–94

  28. Le Q, Mikolov T (2014) Distributed representations of sentences and documents. In: International conference on machine learning, pp 1188–1196

  29. Hill F, Cho K, Korhonen A (2016) Learning distributed representations of sentences from unlabelled data. arXiv preprint arXiv:1602.03483

  30. Kiros R, Zhu Y, Salakhutdinov RR, Zemel R, Urtasun R, Torralba A, Fidler S (2015) Skip-thought vectors. In: Advances in neural information processing systems, pp 3294–3302

  31. Logeswaran L, Lee H (2018) An efficient framework for learning sentence representations. arXiv preprint arXiv:1803.02893

  32. Tang S, Jin H, Fang C, Wang Z, de Sa VR (2017) Speeding up context-based sentence representation learning with non-autoregressive convolutional decoding. arXiv preprint arXiv:1710.10380

  33. Narayan R, Manan R, Dash S (2016) Ensemble based hybrid machine learning approach for sentiment classification—a review. Int J Comput Appl 146(6):31–36

    Google Scholar 

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Acknowledgements

This research was supported by Research Foundation of Education Bureau of Hubei Province with Grant No. D20172502. We thank the peer reviewers for great comments.

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Authors and Affiliations

  1. College of Computer Science Technology, Hubei Normal University, Huangshi, China

    Guangmin Li

  2. School of Computing, Ulster University, Belfast, BT37 0QB, UK

    Zhiwei Lin, Hui Wang & Xin Wei

Authors
  1. Guangmin Li
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  2. Zhiwei Lin
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  3. Hui Wang
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  4. Xin Wei
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Correspondence to Guangmin Li.

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Li, G., Lin, Z., Wang, H. et al. A Discriminative Approach to Sentiment Classification. Neural Process Lett 51, 749–758 (2020). https://doi.org/10.1007/s11063-019-10108-7

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