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Stance Analysis for Debates on Traditional Chinese Medicine at Tianya Forum

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Computational Social Networks (CSoNet 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9795))

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Abstract

Internet and social media devices have created a new public space for debates on societal topics. This paper applies text mining methods to conduct stance analysis of on-line debates with the illustration of debates on traditional Chinese medicine (TCM) at one famous Chinese BBS Tianya Froum. After crawling and preprocessing data, logistic regression is adopted to get a domain lexicon. Words in the lexicon are taken as features to automatically distinguish stances. Furthermore a topic model latent Dirichlet allocation (LDA) is utilized to discover shared topics of different camps. Then further analysis is conducted to detect the focused technical terms of TCM and human names referred during the debates. The classification results reveal that using domain discriminating words as features of classifier outperforms taking nouns, verbs, adjectives and adverbs as features. The results of topic modeling and further analysis enable us to see how the different camps express their stances.

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Notes

  1. 1.

    http://www.cs.cornell.edu/people/pabo/movie-review-data/.

  2. 2.

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

  3. 3.

    http://ictclas.nlpir.org/.

  4. 4.

    http://pinyin.sogou.com/dict/detail/index/20664.

  5. 5.

    http://www.yelab.net/software/SLEP/.

  6. 6.

    http://cran.r-project.org/web/packages/e1071/.

  7. 7.

    http://pinyin.sogou.com/dict/detail/index/20664.

References

  1. Riloff, E.: Automatically generating extraction patterns from untagged text. In: 13th National Conference on Artificial Intelligence, Portland, pp. 1044–1049 (1996)

    Google Scholar 

  2. Riloff, E., Wiebe, J.: Learning extraction patterns for subjective expressions. In: Conference on Empirical Methods in Natural Language Processing, Sapporo, pp. 105–112 (2003)

    Google Scholar 

  3. Cui, H., Mittal, V., Datar, M.: Comparative experiments on sentiment classification for online product reviews. In: 21st National Conference on Artificial Intelligence, Boston, pp. 61–80 (2006)

    Google Scholar 

  4. Ng, V., Dasgupta, S., Arifin, S.M.N.: Examining the role of linguistic knowledge sources in the automatic identification and classification of reviews. In: International Conference on Computational Linguistics and Meeting of the Association for Computational Linguistics, Sydney, pp. 381–393 (2006)

    Google Scholar 

  5. Gamon, M.: Sentiment classification on customer feedback data: noisy data, large feature vectors, and the role of linguistic analysis. In: 23rd International Conference on Computational Linguistics, Beijing, pp. 841–847 (2010)

    Google Scholar 

  6. Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? sentiment classification using machine learning techniques. In: Conference on Empirical Methods in Natural Language Processing, Philadelphia, pp. 79–86 (2009)

    Google Scholar 

  7. Liu, B.: Opinion mining and sentiment analysis. Found. Trends Inf. Retrieval 2(1–2), 1–135 (2008)

    Google Scholar 

  8. Zhao, Y.L., Tang, X.J.: In-depth analysis of online hot discussion about TCM. In: 15th International Symposium on Knowledge and Systems Science, pp. 275–283. JAIST Press, Sapporo (2014)

    Google Scholar 

  9. Turney, P.D., Littman, M.L.: Measuring praise and criticism: inference of semantic orientation from association. ACM Trans. Inf. Syst. 21(4), 315–346 (2003)

    Article  Google Scholar 

  10. Somasundaran, S., Wiebe, J.: Recognizing stances in ideological on-line debates. In: NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, Los Angeles, pp. 116–124 (2010)

    Google Scholar 

  11. Abbott, R., Walker, M., Anand, P., et al.: How can you say such things?!?: recognizing disagreement in informal political argument. In: Workshop on Languages in Social Media, Portland, pp. 2–11 (2011)

    Google Scholar 

  12. Anand, P., Walker, M., Abbott, R., et al.: Cats rule and dogs drool!: classifying stance in online debate. In: 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis, Portland, pp. 1–9 (2011)

    Google Scholar 

  13. Walker, M.A., Anand, P., Abbott, R., et al.: That is your evidence? Classifying stance in online political debate. Decis. Support Syst. 53(4), 719–729 (2012)

    Article  Google Scholar 

  14. Tikves, S., Banerjee, S., Temkit, H., et al.: A system for ranking organizations using social scale analysis. Soc. Netw. Anal. Min. 3(3), 313–328 (2013)

    Article  Google Scholar 

  15. Tikves, S., Gokalp, S., Temkit, M., et al.: Perspective analysis for online debates. In: International Conference on Advances in Social Networks Analysis and Mining, Istanbul, pp. 898–905 (2012)

    Google Scholar 

  16. Gryc, W., Moilanen, K.: Leveraging textual sentiment analysis with social network modeling: sentiment analysis of political blogs in the 2008 U.S. Presidential Election. In: Workshop on from Text to Political Positions, Amsterdam (2010)

    Google Scholar 

  17. Lin, W.H., Wilson, T., Wiebe, J.: Which side are you on? Identifying perspectives at the document and sentence levels. In: 10th Conference on Computational Natural Language Learning, New York, pp. 109–116 (2006)

    Google Scholar 

  18. Hatzivassiloglou, V., Wiebe, J.M.: Effects of adjective orientation and gradability on sentence subjectivity. In: International Conference on Computational Linguistics, Mexico, pp. 299–305 (2003)

    Google Scholar 

  19. Benamara, F., Cesarano, C., Picariello, A., et al.: Sentiment analysis: adjectives and adverbs are better than adjectives alone. In: Veselovská, K., Hajic, J., Šindlerová Bojar, O., Žabokrtský, Z. (eds.) International Conference on Weblogs and Social Media, Boulder (2007)

    Google Scholar 

  20. Subrahmanian, V.S., Reforgiato, D.: AVA: Adjective-verb-adverb combinations for sentiment analysis. IEEE Intell. Syst. 23(4), 43–50 (2008)

    Article  Google Scholar 

  21. Shen, J., Zhu, P., Fan, R., Tan, W., Zhan, X.: Sentiment analysis based on user tags for traditional Chinese medicine in Weibo. In: Li, J., et al. (eds.) NLPCC 2015. LNCS, vol. 9362, pp. 134–145. Springer, Heidelberg (2015). doi:10.1007/978-3-319-25207-0_12

    Chapter  Google Scholar 

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

    MATH  Google Scholar 

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (Nos. 61473284 and 71371107).

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Authors and Affiliations

  1. Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, People’s Republic of China

    Can Wang & Xijin Tang

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  1. Can Wang
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  2. Xijin Tang
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Correspondence to Can Wang .

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Editors and Affiliations

  1. Ton Duc Thang University , Ho Chi Minh City, Vietnam

    Hien T. Nguyen

  2. VSB-Technical University of Ostrava , Ostrava, Poland

    Vaclav Snasel

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Wang, C., Tang, X. (2016). Stance Analysis for Debates on Traditional Chinese Medicine at Tianya Forum. In: Nguyen, H., Snasel, V. (eds) Computational Social Networks. CSoNet 2016. Lecture Notes in Computer Science(), vol 9795. Springer, Cham. https://doi.org/10.1007/978-3-319-42345-6_28

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  • DOI: https://doi.org/10.1007/978-3-319-42345-6_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42344-9

  • Online ISBN: 978-3-319-42345-6

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