Topic-sentiment evolution over time: a manifold learning-based model for online news

Abstract

Topic and sentiment detection has been considered as an effective method to reveal the facts and sentiments in a massive volume of information. Existing works mainly focus on separate topic and sentiment extraction or static topic-sentiment associations, neglecting topic-sentiment dynamics and missing the opportunity to provide a in-depth analysis of online news. Actually, sentiment orientations are highly dependent on topic content and thus detecting topic-sentiment associations and their evolution over time is very important. This paper proposes a manifold learning-based model to explore the topic-sentiment associations and their evolution over time in the online news domain. The proposed model can visualize the hidden sentiment dynamics of topics in a low-dimensional space. Extensive experiments are conducted on online news crawled from the American Cable News Networks (CNN) website. The experimental results show that the proposed model outperforms the KL distance-based and the Similarity-based methods and improves the accuracy of topic classification by 12%.

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    http://www.cs.pitt.edu/mpqa/

References

  1. Benjamin, D.J., Berger, J.O., Johannesson, M., et al. (2018). Redefine statistical significance. Nature Human Behaviour, 2, 6–10.

    Article  Google Scholar 

  2. Bing, L., & Lei, Z. (2012). A survey of opinion mining and sentiment analysis. In Mining text data (pp. 415–463): Springer.

  3. Bo, P., & Lillian, L. (2008). Opinion mining and sentiment analysis. In Journal of foundations and trends in information retrieval, (Vol. 2 pp. 1–135).

  4. Bu, Z., Zhang, C., Xia, Z., Wang, J. (2013). A fast parallelmodularity optimization algorithm (fpmqa) for community detection in online social network. Knowledge-Based Systems, 50, 246–259.

    Article  Google Scholar 

  5. Dermouche, M., Velcin, J., Khouas, L., Loudcher, S. (2014). A joint model for topic-sentiment evolution over time. In IEEE international conference on data mining (pp. 773–778): IEEE.

  6. Gong, C., Tao, D., Liu, W. (2016). Label propagation via teaching-to-learn and learning-to-teach. IEEE Transactions on Neural Networks and Learning Systems, 1452–1465.

  7. Hoffman, M., Bach, F.R., Blei, D.M. (2010). Online learning for latent dirichlet allocation. In Proceedings of the neural information processing systems conference (pp. 993–1022).

  8. Hofmann, T. (2017). Probabilistic latent semantic indexing. In SIGIR Forum-SIGIR test-of-time awardees 1978-2001 (pp. 211–218): ACM.

  9. Hout, M.C., Papesh, M.H., Goldinger, S.D. (2013). Multidimensional scaling. Wires Cognitive Science, 4(1), 93–103.

    Article  Google Scholar 

  10. Jo, Y., & Oh, A.H. (2011). 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). New York: ACM.

  11. Juan, C., Tian, X., Li, J. (2009). A density based method for adaptive lda model selection. Neuro Computing, 72(7), 1775–1781.

    Google Scholar 

  12. Keming, C., & Fang, L. (2011). Lda model-based news topic evolution. Computer Application and software, 28(4), 4–8.

    Google Scholar 

  13. Li, F., Huang, M., Zhu, X. (2010). Sentiment analysis with global topics and local dependency. In Proceedings of the twenty-fourth AAAI conference on artificial intelligence. Association for the Advancement of Artificial Intelligence (pp. 1371–1376).

  14. Lin, C., & He, Y. (2009). Joint sentiment/topic model for sentiment analysis. In Proceedings of the 18th ACM conference on Information and knowledge management (pp. 375–384).

  15. Lin, C., He, Y., Everson, R., Ruger, S. (2012). Weakly supervised joint sentiment-topic detection from text. In IEEE transactions on knowledge and data engineering, (Vol. 24 pp. 424–433): IEEE.

  16. Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies, 1–143.

  17. Mei, Q., Ling, X., Wondra, M., Su, H., Zhai, C.X. (2007). Topic sentiment mixture: modeling facets and opinions in weblogs. In Proceedings of the 16th international conference on World Wide Web (pp. 171–180). New York: ACM.

  18. Oikawa, M.A., Dias, Z., de Rezende Rocha, A. (2016). Manifold learning and spectral clustering for image phylogeny forests. IEEE Transactions on Information Forenstcs and Security, 11(1), 5–19.

    Article  Google Scholar 

  19. Roweis, S.T., & Saul, L.K. (2000). Nonlinear dimensionality reduction by locally linear embedding. Science, 290(5500), 2323–2326.

    Article  Google Scholar 

  20. Sainani, K.L. (2014). Introduction to principal components analysis. PMR, 6(3), 275–278.

    Article  Google Scholar 

  21. Tomar, V.S., & Rose, R.C. (2014). Multi-feature multi-manifold learning for single-sample face recognition. Neurocomputing, 143(2), 134–143.

    Google Scholar 

  22. Wang, X., & McCallum, A. (2006). Topics over time: a non-markov continuous-time model of topical trends. KDD’06. Philadelphia, PA, USA, 424–433.

  23. Wang, Q., Lin, J., Yuan, Y. (2016). Salient band selection for hyperspectral image classification via manifold ranking. IEEE Transactions on Neural Networks and Learning Systems, 27(6), 7606–7618.

    Article  Google Scholar 

  24. Wiebe, J., Wilson, T., Cardie, C. (2005). Annotating expressions of opinions and emotions in language. Language Resources and Evaluation, 39(23), 165–210.

    Article  Google Scholar 

  25. Yan, H., Lu, J., Zhou, X. (2013). Efficient manifold learning for speech recognition using locality sensitive hashing. IEEE International Conference on Acoustics, Speech and Signal Processing, 1324–1331.

  26. Zhang, Z., Chow, T.W.S., Zhao, M. (2013). M-isomap: orthogonal constrained marginal isomap for nonlinear dimensionality reduction. IEEE Transactions on Cybernetics, 43(1), 180–191.

    Article  Google Scholar 

  27. Zhu, X., Han, W., Chen, W. (2015). Gridgraph: large-scale graph processing on a single machine using 2-level hierarchical partitioning. In 2015 USENIX annual technical conference. USENIX (pp. 375–386).

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Correspondence to Yuemei Xu.

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This work is supported by the Supported by Beijing Municipal Social Science Foundation (No.15JDZHC011), the project of Double Top-Class Foundation of BFSU (No.YY19ZZA012).

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Xu, Y., Li, Y., Liang, Y. et al. Topic-sentiment evolution over time: a manifold learning-based model for online news. J Intell Inf Syst 55, 27–49 (2020). https://doi.org/10.1007/s10844-019-00586-5

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Keywords

  • Topic-sentiment association
  • Sentiment evolution
  • Manifold learning