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.
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Available at: http://snowball.tartarus.org/.
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References
Blei, D., Ng, A., Jordan, M.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Brody, S., Diakopoulos, N.: Cooooooollllllllll!!!!!!!!!: using word lengthening to detect sentiment in microblogs. In: EMNLP 2011, pp. 562–570 (2011)
Eguchi, K., Lavrenko, V.: Sentiment retrieval using generative models. In: EMNLP 2006, pp. 345–354 (2006)
Go, A., Bhayani, R., Huang, L.: Technical report, Standford (2009)
Hofmann, T.: Probabilistic latent semantic indexing. In: SIGIR 1999, pp. 50–57 (1999)
Hong, L., Davison, B.D.: Empirical study of topic modeling in Twitter. In: SIGKDD Workshop on SMA, pp. 80–88 (2010)
Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: ACL, HLT 2011, pp. 151–160 (2011)
Kouloumpis, E., Wilson, T., Moore, J.: Twitter sentiment analysis: The good the bad and the omg!. In: ICWSM 2011, pp. 538–541 (2011)
Krestel, R., Fankhauser, P., Nejdl, W.: Latent dirichlet allocation for tag recommendation. In: RecSys 2009, pp. 61–68 (2009)
Luo, Z., Osborne, M., Wang, T.: An effective approach to tweets opinion retrieval. In: WWW 2013, pp. 1–22 (2013)
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)
Pak, A., Paroubek, P.: Twitter as a corpus for sentiment analysis and opinion mining. In: LREC 2010, pp. 1320–1326 (2010)
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)
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)
Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retr. 2(1–2), 1–135 (2008)
Porter, M.: An algorithm for suffix stripping. Program 14(3), 130–137 (1980)
Ramage, D., Dumais, S., Liebling, D.: Characterizing microblogs with topic models. In: ICWSM 2010, pp. 1–8 (2010)
Read, J.: Using emoticons to reduce dependency in machine learning techniques for sentiment classification. In: ACL Student Research Workshop, pp. 43–48 (2005)
Strunk, W.: The Elements of Style. Penguin, New York (2007)
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)
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)
Yao, L., Mimno, D., McCallum, A.: Efficient methods for topic model inference on streaming document collections. In: SIGKDD 2009, pp. 937–946 (2009)
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)
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)
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This research was partially funded by the Swiss National Science Foundation (SNSF) under the project OpiTrack.
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Giachanou, A., Harvey, M., Crestani, F. (2016). Topic-Specific Stylistic Variations for Opinion Retrieval on Twitter. In: Ferro, N., et al. Advances in Information Retrieval. ECIR 2016. Lecture Notes in Computer Science(), vol 9626. Springer, Cham. https://doi.org/10.1007/978-3-319-30671-1_34
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DOI: https://doi.org/10.1007/978-3-319-30671-1_34
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