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Topic-Based Microblog Polarity Classification Based on Cascaded Model

  • Quanchao Liu
  • Yue Hu
  • Yangfan Lei
  • Xiangpeng Wei
  • Guangyong Liu
  • Wei Bi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10861)

Abstract

Given a microblog post and a topic, it is an important task to judge the sentiment towards that topic: positive or negative, and has important theoretical and application value in the public opinion analysis, personalized recommendation, product comparison analysis, prevention of terrorist attacks, etc. Because of the short and irregular messages as well as containing multifarious features such as emoticons, and sentiment of a microblog post is closely related to its topic, most existing approaches cannot perfectly achieve cooperating analysis of topic and sentiment of messages, and even cannot know what factors actually determined the sentiment towards that topic. To address the issues, MB-LDA model and attention network are applied to Bi-RNN for topic-based microblog polarity classification. Our cascaded model has three distinctive characteristics: (i) a strong relationship between topic and its sentiment is considered; (ii) the factors that affect the topic’s sentiment are identified, and the degree of influence of each factor can be calculated; (iii) the synchronized detection of the topic and its sentiment in microblog is achieved. Extensive experiments show that our cascaded model outperforms state-of-the-art unsupervised approach JST and supervised approach SSA-ST significantly in terms of sentiment classification accuracy and F1-Measure.

Keywords

Cascaded model Attention model LDA model Bi-RNN Sentiment analysis Microblog topic 

Notes

Acknowledgments

This paper is financially supported by The National Key Research and Development Program of China (No. 2017YFB0803003) and National Science Foundation for Young Scientists of China (No. 6170060558). We would like to thank the anonymous reviewers for many valuable comments and helpful suggestions. Our future work will be carried out in the following aspects: firstly, the file attribute information of microblog users is incorporated into microblog message emotional polarity and thematic reasoning in order to improve the accuracy of polarity classification; Secondly, more explicit emotional features are excavated into the attention network to improve the accuracy of the polarity classification.

References

  1. 1.
    Deerwester, S., Dumais, S.T., Furnas, G.W., et al.: Indexing by latent semantic analysis. J. Assoc. Inf. Sci. Technol. 41(6), 391–407 (1990)Google Scholar
  2. 2.
    Hofmann, T.: Probabilistic latent semantic indexing. In: International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 50–57. ACM (1999)Google Scholar
  3. 3.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. Arch. 3, 993–1022 (2003)MATHGoogle Scholar
  4. 4.
    Blei, D.M., Lafferty, J.D.: Dynamic topic models. In: International Conference, DBLP, pp. 113–120 (2006)Google Scholar
  5. 5.
    Boydgraber, J., Blei, D.M.: Syntactic topic models. In: Advances in Neural Information Processing Systems, pp. 185–192 (2008)Google Scholar
  6. 6.
    Nallapati, R., Cohen, W.: Link-PLSA-LDA: a new unsupervised model for topics and influence of blogs. In: ICWSM (2008)Google Scholar
  7. 7.
    Sun, C., Gao, B., Cao, Z., et al.: HTM: a topic model for hypertexts. In: Conference on Empirical Methods in Natural Language Processing, pp. 514–522. Association for Computational Linguistics (2008)Google Scholar
  8. 8.
    Go, A., Bhayani, R., Huang, L.: Twitter sentiment classification using distant supervision. CS224N Project report, Stanford (2009)Google Scholar
  9. 9.
    Barbosa, L., Feng, J.: Robust sentiment detection on Twitter from biased and noisy data. In: Proceedings of COLING 2010 Beijing, China, pp. 36–44 (2010)Google Scholar
  10. 10.
    Long, J., Yu, M., Zhou, M., et al.: Target-dependent Twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, Portland, Oregon, pp. 151–160 (2011)Google Scholar
  11. 11.
    Du, W., Tan, S., Yun, X., et al.: A new method to compute semantic orientation. J. Comput. Res. Dev. 46(10), 1713–1720 (2009)Google Scholar
  12. 12.
    Liu, Q., Feng, C., Huang, H.: Emotional tendency identification for micro-blog topics based on multiple characteristics. In: 26th Pacific Asia Conference on Language, Information and Computation (PACLIC 26), pp. 280–288 (2012)Google Scholar
  13. 13.
    Wang, S., Li, D., Wei, Y.: A method of text sentiment classification based on weighted rough membership. J. Comput. Res. Dev. 48(5), 855–861 (2011)Google Scholar
  14. 14.
    Agarwal, A., Xie, B., Vovsha, I., et al.: Sentiment analysis of Twitter data. In: Proceedings of the Workshop on Language in Social Media (LSM 2011), Portland, Oregon, pp. 30–38 (2011)Google Scholar
  15. 15.
    Zhang, C., Sun, J., Ding, Y.: Topic mining for microblog based on MB-LDA model. J. Comput. Res. Dev. 48(10), 1795–1802 (2011)Google Scholar
  16. 16.
    Yang, Z., Yang, D., Dyer, C., et al.: Hierarchical attention networks for document classification. In: Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 1480–1489 (2017)Google Scholar
  17. 17.
    Lin, C., He, Y., Everson, R., et al.: Weakly supervised joint sentiment-topic detection from text. IEEE Trans. Knowl. Data Eng. 24(6), 1134–1145 (2012)CrossRefGoogle Scholar
  18. 18.
    Hu, X., Tang, L., Tang, J., et al.: Exploiting social relation for sentiment analysis in microblogging. In: Proceedings of the 6th International Conference on Web Search and Data Mining. Rome, Italy, pp. 537–546 (2013)Google Scholar
  19. 19.
    Nakov, P.: Semantic sentiment analysis of Twitter data. arXiv preprint arXiv:1710.01492 (2017)Google Scholar
  20. 20.
    Wang, B., Liakata, M., Tsakalidis, A., et al.: TOTEMSS: topic-based, temporal sentiment summarisation for Twitter. In: Proceedings of the IJCNLP 2017, System Demonstrations, pp. 21–24 (2017)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Quanchao Liu
    • 1
    • 2
  • Yue Hu
    • 1
    • 2
  • Yangfan Lei
    • 2
  • Xiangpeng Wei
    • 2
  • Guangyong Liu
    • 4
  • Wei Bi
    • 3
  1. 1.Institute of Information EngineeringChinese Academy of ScienceBeijingChina
  2. 2.University of Chinese Academy of ScienceBeijingChina
  3. 3.SeeleTech CorporationSan FranciscoUSA
  4. 4.BeijingChina

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