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Reputation Analysis Based on Weakly-Supervised Bi-LSTM-Attention Network

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Intelligent Systems and Applications (IntelliSys 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 294))

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Abstract

Due to the emergence of rapid, mass-produced information in the Web2.0 era, a large amount of weakly labeled information (star ratings, etc.) has been widespread. The Weakly-Supervised Deep Embedding (WDE) model is a good choice for utilizing this kind of data. The ratings are treated as weakly-supervised signals for pre-training, fine tuning the whole model with a small amount of manually labeled data. In this research, we proposed to change the original unidirectional transmission into bidirectional in the LSTM layer to capture the semantics in both directions, and an attention mechanism is introduced, which is helpful to capture the important information in the context and improve the accuracy of sentiment classification. Finally, we use TF-IDF and LDA topic models to mine the review topics and extract the consumers’ opinions on different sentiment polarities.

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References

  1. Piryani, R., Gupta, V., Singh, V.K.: Movie Prism: A novel system for aspect level sentiment profiling of movies. J. Intell. Fuzzy Syst. 32(5), 3297–3311 (2017)

    Article  Google Scholar 

  2. Araque, O., Corcuera-Platas, I., Sánchez-Rada, J.F., Iglesias, C.A.: Enhancing deep learning sentiment analysis with ensemble techniques in social applications. Expert Syst. Appl. 77, 236–246 (2017)

    Article  Google Scholar 

  3. Chen, L., Martineau, J., Cheng, D., Sheth, A.: Clustering for simultaneous extraction of aspects and features from reviews. In: NAACL-HLT, pp. 789–799 (2016)

    Google Scholar 

  4. Ren, J., Li, X., Sheng, C., Gao, Y.: Research on attachment behavior of tourism E-commerce websites based on the goal-framing theory. China Acad. J. Electron. Publ. House (C), 107–108 (2012)

    Google Scholar 

  5. Zhao, W., Guan, Z., Chen, L., et al.: Weakly-supervised deep embedding for product review sentiment analysis. IEEE Trans. Knowl. Data Eng. 30(1), 185–197 (2018)

    Article  Google Scholar 

  6. Bengio, Y., Courville, A., Vincent, P.: Representation learning: a review and new perspectives. IEEE TPAMI 35(8), 1798–1828 (2013)

    Article  Google Scholar 

  7. Bengio, Y.: Learning deep architectures for AI. Found. Trends Mach. Learn. 2(1), 1–127 (2009)

    Article  MathSciNet  Google Scholar 

  8. Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, New York (2006)

    MATH  Google Scholar 

  9. Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., Kuksa, P.: Natural language processing from scratch . JMLR 12, 2493–2537 (2011)

    MATH  Google Scholar 

  10. Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Netw. 18(5), 602–610 (2005)

    Article  Google Scholar 

  11. Tang, D., Wei, F., Yang, N., Zhou, M., Liu, T., Qin, B.: Learning sentiment-specific word embedding for Twitter sentiment classification. In: ACL, vol. 1, pp. 1555–1565 (2014)

    Google Scholar 

  12. Thet, T., et al.: Aspect-Based Sentiment Analysis of Movie Reviews on Discussion Boards, pp. 823–848. SAGE Publications, London (2010)

    Google Scholar 

  13. Taboada, M., Brooke, J., et al.: Lexicon-based methods for sentiment analysis . Comput. Linguist. 37(2), 267–307 (2011)

    Article  Google Scholar 

  14. Pang, B., Lee, L., Vaithyanathan, S.: Sentiment classification using machine learning techniques. In: Proceedings of Empirical Methods in Natural Language Processing, pp. 79–86. MIT Press, Cambridge (2002)

    Google Scholar 

  15. Turney, D.: Semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, pp. 417–424 (2002)

    Google Scholar 

  16. Sanjiv, D., Mike, C.: Yahoo! for Amazon: sentiment parsing from small talk on the web. In: Proceedings of the 8th Asia Pacific Finance Association Annual Conference (2001)

    Google Scholar 

  17. Fei, Z., Liu, J., Wu, G.: Sentiment classification using phrase patterns. In: The Fourth International Conference on Computer and Information Technology, CIT 2004, pp. 1147–1152. IEEE (2004)

    Google Scholar 

  18. Dave, K., Lawrence, S., Pennock, M.: Mining the peanut gallery: opinion extraction and semantic classification of product reviews. In: Proceedings of the 12th International Conference on World Wide Web, pp. 519–528. ACM (2003)

    Google Scholar 

  19. Li, C., Huimin, W., Jin, Q.: Emotion classification of chinese microblog text via fusion of BoW and eVector feature representations. In: Zong, C., Nie, J.-Y., Zhao, D., Feng, Y. (eds.) NLPCC 2014. CCIS, vol. 496, pp. 217–228. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-45924-9_20

    Chapter  Google Scholar 

  20. Medhat, W., Hassan, A., Korashy, H.: Sentiment analysis algorithms and applications: a survey. Ain Shams Eng. J. 5(4), 1093–1113 (2014)

    Article  Google Scholar 

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

    Article  Google Scholar 

  22. Dave, K., Lawrence, S., Pennock, D.M.: Mining the peanut gallery: opinion extraction and semantic classification of product reviews. In: WWW, pp. 519–528 (2003)

    Google Scholar 

  23. Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K., Harshman, R.: Indexing by latent semantic analysis. J. Am. Soc. Inf. Sci. 41(6), 391 (1990)

    Article  Google Scholar 

  24. Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: WSDM, pp. 231–240 (2008)

    Google Scholar 

  25. Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent Twitter sentiment classification. In: ACL, pp. 49–54 (2014)

    Google Scholar 

  26. Duchi, J., Hazan, E., Singer, Y.: Adaptive subgradient methods for online learning and stochastic optimization. JMLR 12, 2121–2159 (2011)

    MathSciNet  MATH  Google Scholar 

  27. Feldman, R.: Techniques and applications for sentiment analysis. Commun. ACM 56(4), 82–89 (2013)

    Article  Google Scholar 

  28. Glorot, X., Bordes, A., Bengio, Y.: Domain adaptation for large-scale sentiment classification: a deep learning approach. In: ICML, pp. 513–520 (2011)

    Google Scholar 

  29. Halpin, H., Robu, V., Shepherd, H.: The complex dynamics of collaborative tagging. In: WWW, pp. 211–220 (2007)

    Google Scholar 

  30. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)

    Article  Google Scholar 

  31. Hu, M., Liu, B.: Mining and summarizing customer reviews. In: SIGKDD, pp. 168–177 (2004)

    Google Scholar 

  32. Kalchbrenner, N., Grefenstette, E., Blunsom, P.: A convolutional neural network for modelling sentences. In: ACL (2014)

    Google Scholar 

  33. Kim, Y.: Convolutional neural networks for sentence classification. In: EMNLP, pp. 1746–1751 (2014)

    Google Scholar 

  34. Kiros, R., et al.: Skip-thought vectors. In: NIPS, pp. 3294–3302 (2015)

    Google Scholar 

  35. Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv:1409.0473 (2014)

  36. Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. In: Proceedings of the Conference on Empirical Methods on Natural Language Processing, pp. 214–224 (2016)

    Google Scholar 

  37. Pham, D.H., Le, A.C.: Learning multiple layers of knowledge representation for aspect based sentiment analysis. Data Knowl. Eng. 114, 26–39 (2017)

    Article  Google Scholar 

  38. Cheng, J., Zhao, S., Zhang, J., King, I., Zhang, X., Wang, H.: Aspect-level sentiment classification with heat (hierarchical attention) network. In: Proceedings of the Conference on Information and Knowledge Management, pp. 97–106. ACM (2017)

    Google Scholar 

  39. Chen, P., Sun, Z., Bing, L., Yang, W.: Recurrent attention network on memory for aspect sentiment analysis. In: Proceedings of the EMNLP, pp. 452–461 (2017)

    Google Scholar 

  40. Ma, D., Li, S., Zhang, X., Wang, H.: Interactive attention networks for aspect-level sentiment classification. In: Proceedings of the IJCAI, pp. 4068–4074 (2017)

    Google Scholar 

  41. Liu, B.: Sentiment Analysis and Opinion Mining. Morgan & Claypool Publishers (2012)

    Google Scholar 

  42. Liu, B., Hu, M., Cheng, J.: Opinion observer: analyzing and comparing opinions on the web. In: WWW, pp. 342–351 (2005)

    Google Scholar 

  43. Maas, A.L., Daly, R.E., Pham, P.T., Huang, D., Ng, A.Y., Potts, C.: Learning word vectors for sentiment analysis. In: ACL, pp. 142–150 (2011)

    Google Scholar 

  44. Nguyen, T.H., Shirai, K.: PhraseRNN: phrase recursive neural network for aspect-based sentiment analysis. In: EMNLP, pp. 2509–2514 (2015)

    Google Scholar 

  45. Qiu, X., Huang, X.: Convolutional neural tensor network architecture for community-based question answering. In: IJCAI, pp. 1305–1311 (2015)

    Google Scholar 

  46. Farman, A., Daehan, K., Pervez, K.: Transportation sentiment analysis using word embedding and ontology-based topic modeling. Knowl.-Based Syst. 174, 27–42 (2019)

    Article  Google Scholar 

  47. Socher, R., Bengio, B., Manning, C.: Deep learning for NLP. In: ACL (Tutorial Abstracts), p. 5 (2012)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Science and Technology, HOSEI University, Koganei, Tokyo, Japan

    Kun Xiang & Akihiro Fujii

Authors
  1. Kun Xiang
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  2. Akihiro Fujii
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Corresponding author

Correspondence to Kun Xiang .

Editor information

Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

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© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Cite this paper

Xiang, K., Fujii, A. (2022). Reputation Analysis Based on Weakly-Supervised Bi-LSTM-Attention Network. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2021. Lecture Notes in Networks and Systems, vol 294. Springer, Cham. https://doi.org/10.1007/978-3-030-82193-7_4

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