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Dynamic Topic-Based Sentiment Analysis of Large-Scale Online News

Part of the Lecture Notes in Computer Science book series (LNISA,volume 10042)

Abstract

Many of today’s online news websites and aggregator apps have enabled users to publish their opinions without respect to time and place. Existing works on topic-based sentiment analysis of product reviews cannot be applied to online news directly because of the following two reasons: (1) The dynamic nature of news streams require the topic and sentiment analysis model also to be dynamically updated. (2) The user interactions among news comments can easily lead to inaccurate topic and sentiment extraction. In this paper, we propose a novel probabilistic generative model (DTSA) to extract topics and the specified sentiments from news streams and analyze their evolution over time simultaneously. DTSA incorporates a multiple timescale model into a generative topic model. Additionally, we further consider the links among news comments to avoid the error caused by user interactions. Finally, we derive distributed online inference procedures to update the model with newly arrived data and show the effectiveness of our proposed model on real-world data sets.

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Notes

  1. 1.

    http://open-platform.theguardian.com/.

  2. 2.

    http://apiwiki.twitter.com/.

  3. 3.

    http://nlp.stanford.edu/software/tagger.shtml.

  4. 4.

    http://sentiwordnet.isti.cnr.it/.

References

  1. Lin, C., He, Y.: Joint sentiment/topic model for sentiment analysis. In: Proceedings of the 18th ACM Conference on Information and Knowledge Management, pp. 375–384. ACM (2009)

    Google Scholar 

  2. Jo, Y., Oh, A.H.: Aspect and sentiment unification model for online review analysis. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, pp. 815–824. ACM (2011)

    Google Scholar 

  3. Li, C., Zhang, J., Sun, J.T., et al.: Sentiment topic model with decomposed prior. In: SIAM International Conference on Data Mining (SDM 2013). Society for Industrial and Applied Mathematics (2013)

    Google Scholar 

  4. Balahur, A., Steinberger, R., Kabadjov, M., et al.: Sentiment analysis in the news (2013). arXiv preprint: arXiv:1309.6202

  5. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)

    MATH  Google Scholar 

  6. Blei, D.M.: Probabilistic topic models. Commun. ACM 55(4), 77–84 (2012)

    Article  MathSciNet  Google Scholar 

  7. Titov, I., McDonald, R.: Modeling online reviews with multi-grain topic models. In: Proceedings of the 17th International Conference on World Wide Web, pp. 111–120. ACM (2008)

    Google Scholar 

  8. Kim, S., Zhang, J., Chen, Z., et al.: A hierarchical aspect-sentiment model for online reviews. In: AAAI (2013)

    Google Scholar 

  9. Zhao, Y., Dong, S., Li, L.: Sentiment analysis on news comments based on supervised learning method. Int. J. Multimed. Ubiquit. Eng. 9, 333–346 (2014)

    Article  Google Scholar 

  10. Wang, C., Blei, D., Heckerman, D.: Continuous time dynamic topic models (2012). arXiv preprint: arXiv:1206.3298

  11. Wang, X., McCallum, A.: Topics over time: a non-Markov continuous-time model of topical trends. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 424–433. ACM (2006)

    Google Scholar 

  12. Iwata, T., Yamada, T., Sakurai, Y., et al.: Online multiscale dynamic topic models. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 663–672. ACM (2010)

    Google Scholar 

  13. Wang, Y., Agichtein, E., Benzi, M.: TM-LDA: efficient online modeling of latent topic transitions in social media. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 123–131. ACM (2012)

    Google Scholar 

  14. Dermouche, M., Velcin, J., Khouas, L., et al.: A joint model for topic-sentiment evolution over time. In: 2014 IEEE International Conference on Data Mining (ICDM), pp. 773–778. IEEE (2014)

    Google Scholar 

  15. Zheng, M., Wu, C., Liu, Y., et al.: Topic sentiment trend model: modeling facets and sentiment dynamics. In: 2012 IEEE International Conference on Computer Science and Automation Engineering (CSAE), pp. 651–657. IEEE (2012)

    Google Scholar 

  16. Wang, L., Cardie, C.: Improving agreement and disagreement identification in online discussions with a socially-tuned sentiment lexicon. In: ACL 2014, p. 97 (2014)

    Google Scholar 

  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)

    Article  Google Scholar 

  18. Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. Commun. ACM 51(1), 107–113 (2008)

    Article  Google Scholar 

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Correspondence to Peng Liu .

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Liu, P., Gulla, J.A., Zhang, L. (2016). Dynamic Topic-Based Sentiment Analysis of Large-Scale Online News. In: Cellary, W., Mokbel, M., Wang, J., Wang, H., Zhou, R., Zhang, Y. (eds) Web Information Systems Engineering – WISE 2016. WISE 2016. Lecture Notes in Computer Science(), vol 10042. Springer, Cham. https://doi.org/10.1007/978-3-319-48743-4_1

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  • DOI: https://doi.org/10.1007/978-3-319-48743-4_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48742-7

  • Online ISBN: 978-3-319-48743-4

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