Advertisement

Topic-Specific Stylistic Variations for Opinion Retrieval on Twitter

  • Anastasia GiachanouEmail author
  • Morgan Harvey
  • Fabio Crestani
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9626)

Abstract

Twitter has emerged as a popular platform for sharing information and expressing opinions. Twitter opinion retrieval is now recognized as a powerful tool for finding people’s attitudes on different topics. However, the vast amount of data and the informal language of tweets make opinion retrieval on Twitter very challenging. In this paper, we propose to leverage topic-specific stylistic variations to retrieve tweets that are both relevant and opinionated about a particular topic. Experimental results show that integrating topic specific textual meta-communications, such as emoticons and emphatic lengthening in a ranking function can significantly improve opinion retrieval performance on Twitter.

Keywords

Opinion retrieval Microblogs Stylistic variations 

Notes

Acknowledgments

This research was partially funded by the Swiss National Science Foundation (SNSF) under the project OpiTrack.

References

  1. 1.
    Blei, D., Ng, A., Jordan, M.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)zbMATHGoogle Scholar
  2. 2.
    Brody, S., Diakopoulos, N.: Cooooooollllllllll!!!!!!!!!: using word lengthening to detect sentiment in microblogs. In: EMNLP 2011, pp. 562–570 (2011)Google Scholar
  3. 3.
    Eguchi, K., Lavrenko, V.: Sentiment retrieval using generative models. In: EMNLP 2006, pp. 345–354 (2006)Google Scholar
  4. 4.
    Go, A., Bhayani, R., Huang, L.: Technical report, Standford (2009)Google Scholar
  5. 5.
    Hofmann, T.: Probabilistic latent semantic indexing. In: SIGIR 1999, pp. 50–57 (1999)Google Scholar
  6. 6.
    Hong, L., Davison, B.D.: Empirical study of topic modeling in Twitter. In: SIGKDD Workshop on SMA, pp. 80–88 (2010)Google Scholar
  7. 7.
    Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: ACL, HLT 2011, pp. 151–160 (2011)Google Scholar
  8. 8.
    Kouloumpis, E., Wilson, T., Moore, J.: Twitter sentiment analysis: The good the bad and the omg!. In: ICWSM 2011, pp. 538–541 (2011)Google Scholar
  9. 9.
    Krestel, R., Fankhauser, P., Nejdl, W.: Latent dirichlet allocation for tag recommendation. In: RecSys 2009, pp. 61–68 (2009)Google Scholar
  10. 10.
    Luo, Z., Osborne, M., Wang, T.: An effective approach to tweets opinion retrieval. In: WWW 2013, pp. 1–22 (2013)Google Scholar
  11. 11.
    Nielsen, F.: A new ANEW: Evaluation of a word list for sentiment analysis of microblogs. In: ESWC 2011 Workshop on ’Making Sense of Microposts’: Big Things Come in Small Packages, pp. 93–98(2011)Google Scholar
  12. 12.
    Pak, A., Paroubek, P.: Twitter as a corpus for sentiment analysis and opinion mining. In: LREC 2010, pp. 1320–1326 (2010)Google Scholar
  13. 13.
    Paltoglou, G., Buckley, K.: Subjectivity annotation of the microblog 2011 realtime adhoc relevance judgments. In: Serdyukov, P., Braslavski, P., Kuznetsov, S.O., Kamps, J., Rüger, S., Agichtein, E., Segalovich, I., Yilmaz, E. (eds.) ECIR 2013. LNCS, vol. 7814, pp. 344–355. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  14. 14.
    Paltoglou, G., Giachanou, A.: Opinion retrieval: searching for opinions in social media. In: Paltoglou, G., Loizides, F., Hansen, P. (eds.) Professional Search in the Modern World. LNCS, vol. 8830, pp. 193–214. Springer, Heidelberg (2014)Google Scholar
  15. 15.
    Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retr. 2(1–2), 1–135 (2008)CrossRefGoogle Scholar
  16. 16.
    Porter, M.: An algorithm for suffix stripping. Program 14(3), 130–137 (1980)CrossRefGoogle Scholar
  17. 17.
    Ramage, D., Dumais, S., Liebling, D.: Characterizing microblogs with topic models. In: ICWSM 2010, pp. 1–8 (2010)Google Scholar
  18. 18.
    Read, J.: Using emoticons to reduce dependency in machine learning techniques for sentiment classification. In: ACL Student Research Workshop, pp. 43–48 (2005)Google Scholar
  19. 19.
    Strunk, W.: The Elements of Style. Penguin, New York (2007)Google Scholar
  20. 20.
    Van Canneyt, S., Claeys, N., Dhoedt, B.: Topic-dependent sentiment classification on twitter. In: Hanbury, A., Kazai, G., Rauber, A., Fuhr, N. (eds.) ECIR 2015. LNCS, vol. 9022, pp. 441–446. Springer, Heidelberg (2015)Google Scholar
  21. 21.
    Wang, X., Wei, F., Liu, X., Zhou, M., Zhang, M.: Topic sentiment analysis in twitter: a graph-based hashtag sentiment classification approach. In: CIKM 2011, pp. 1031–1040 (2011)Google Scholar
  22. 22.
    Yao, L., Mimno, D., McCallum, A.: Efficient methods for topic model inference on streaming document collections. In: SIGKDD 2009, pp. 937–946 (2009)Google Scholar
  23. 23.
    Yi, J., Nasukawa, T., Bunescu, R., Niblack, W.: Sentiment analyzer: extracting sentiments about a given topic using natural language processing techniques. In: ICDM 2003, pp. 427–434 (2003)Google Scholar
  24. 24.
    Zhao, W.X., Jiang, J., Weng, J., He, J., Lim, E.-P., Yan, H., Li, X.: Comparing twitter and traditional media using topic models. In: Clough, P., Foley, C., Gurrin, C., Jones, G.J.F., Kraaij, W., Lee, H., Mudoch, V. (eds.) ECIR 2011. LNCS, vol. 6611, pp. 338–349. Springer, Heidelberg (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Anastasia Giachanou
    • 1
    Email author
  • Morgan Harvey
    • 2
  • Fabio Crestani
    • 1
  1. 1.Faculty of InformaticsUniversità della Svizzera italiana (USI)LuganoSwitzerland
  2. 2.Department of Maths and Information SciencesNorthumbria UniversityNewcastle upon TyneUK

Personalised recommendations