Topic-Based Stance Mining for Social Media Texts

  • Wei-Fan ChenEmail author
  • Yann-Hui Lee
  • Lun-Wei Ku
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9191)


Recent techniques of opinion mining have succeeded in analyzing sentiment on the social media, but processing the skewed data or data with few labels about political or social issues remains tough. In this paper, we introduce a two-step approach that starts from only five seed words for detecting the stance of Facebook posts toward the anti-reconstruction of the nuclear power plant. First, InterestFinder, which detects interest words, is adopted to filter out irrelevant documents. Second, we employ machine learning methods including SVM and co-training, and also a compositional sentiment scoring tool CopeOpi to determine the stance of each relevant post. Experimental results show that when applying the proposed transition process, CopeOpi outperforms the other machine learning methods. The best precision scores of predicting three stance categories (i.e., supportive, neutral and unsupportive) are 94.62 %, 88.86 % and 10.47 %, respectively, which concludes that the proposed approach can capture the sentiment of documents from lack-of-label, skewed data.


  1. 1.
    Blum, A., Mitchell, T.: Combining labeled and unlabeled data with co-training. In: Proceedings of the Eleventh Annual Conference on Computational Learning Theory, pp. 92–100. ACM (1998)Google Scholar
  2. 2.
    Bollen, J., Mao, H., Pepe, A.: Modeling public mood and emotion: twitter sentiment and socio-economic phenomena. In: Proceedings of ICWSM 2011 (2011)Google Scholar
  3. 3.
    Gao, D., Wei, F., Li, W., Liu, X., Zhou, M.: Co-training based bilingual sentiment lexicon learning. In: AAAI (2013)Google Scholar
  4. 4.
    Hu, X., Tang, J., Gao, H., Liu, H.: Unsupervised sentiment analysis with emotional signals. In: Proceedings of the 22nd International Conference on World Wide Web, WWW 2013. ACM (2013)Google Scholar
  5. 5.
    Hu, Y., Wang, F., Kambhampati, S.: Listening to the crowd: automated analysis of events via aggregated twitter sentiment. In: Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, pp. 2640–2646. AAAI Press (2013)Google Scholar
  6. 6.
    Huang, C., Ku, L.-W. Interest analysis using semantic pagerank and social interaction content. In: Proceedings of the IEEE International Conference on Data Mining, SENTIRE Work-shop (2013)Google Scholar
  7. 7.
    Ku, L.-W., Ho, X.-W., Chen, H.-H.: Opinion mining and relationship discovery using CopeOpi opinion analysis system. J. Am. Soc. Inf. Sci. Technol. 60(7), 1486–1503 (2009)CrossRefGoogle Scholar
  8. 8.
    Li, S., Wang, Z., Zhou, G., Lee, S.Y.M.: Semi-supervised learning for imbalanced sentiment classification. In: Proceedings of International Joint Conference on Artificial Intelligence (IJCAI 2011), vol. 22, No. 3, p. 1826 (2011)Google Scholar
  9. 9.
    Maas, A.L., Daly, R.E., Pham, P.T., Huang, D., Ng, A.Y., Potts, C.: Learning word vectors for sentiment analysis. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, vol. 1, pp. 142–150. Association for Computational Linguistics (2011)Google Scholar
  10. 10.
    Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? Sentiment classification using machine learning techniques. In: Proceedings of EMNLP, pp. 79–86 (2002)Google Scholar
  11. 11.
    Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: Proceedings of the Empiricial Methods in Natural Language Processing (EMNLP 2014), p. 12 (2014)Google Scholar
  12. 12.
    Ringsquandl, M., Petkovic, D.: Analyzing political sentiment on twitter. In: AAAI Spring Symposium: Analyzing Micro-text (2013)Google Scholar
  13. 13.
    Yohei, S., Lun-Wei, K., Le, S., Hsin-Hsi, C., Noriko, K.: Overview of multilingual opinion analysis task at NTCIR-8: a step toward cross lingual opinion analysis. In: Proceedings of the 8th NTCIR Workshop Meeting on Evaluation of Information Access Technologies: Information Retrieval, Question Answering, and Cross-Lingual Information Access, pp. 209-220, Tokyo, Japan 15–18 June 2010Google Scholar
  14. 14.
    Socher, R., Pennington, J., Huang, E.H., Ng, A.Y., Manning, C.D.: Semi-supervised recursive autoencoders for predicting sentiment distributions. In: Proceedings of Conference on Empirical Methods in Natural Language Processing (EMNLP 2011), pp. 151–161 (2011)Google Scholar
  15. 15.
    Wang, X.: Co-training for cross-lingual sentiment classification. In: Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP, vol. 1, pp. 235–243. Association for Computational Linguistics (2009)Google Scholar
  16. 16.
    Wang, Y., Clark, T., Agichtein, E., Staton, J.: Towards tracking political sentiment through microblog data. In: ACL 2014, p. 88 (2014)Google Scholar
  17. 17.
    Yang, Y.H., Liu, J.Y.: Quantitative study of music listening behavior in a social and affective context. IEEE Trans. Multimedia 15(6), 1304–1315 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Institute of Information ScienceAcademia SinicaTaipeiTaiwan

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