Abstract
Twitter has become an important source for people to collect opinions to make decisions. However the amount and the variety of opinions constitute the major challenge to using them effectively. Here we consider the problem of finding propagated opinions – tweets that express an opinion about some topics, but will be retweeted. Within a learning-to-rank framework, we explore a wide of spectrum features, such as retweetability, opinionatedness and textual quality of a tweet. The experimental results show the effectiveness of our features for this task. Moreover the best ranking model with all features can outperform a BM25 baseline and state-of-the-art for Twitter opinion retrieval approach. Finally, we show that our approach equals human performance on this task.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
Bollen, J., Mao, H., Zeng, X.-J.: Twitter mood predicts the stock market. J. Comput. Science 2(1), 1–8 (2011)
Duan, Y., Jiang, L., Qin, T., Zhou, M., Shum, H.Y.: An empirical study on learning to rank of tweets. In: Proceedings of the 23rd International Conference on Computational Linguistics, COLING 2010, pp. 295–303. Association for Computational Linguistics, Stroudsburg (2010)
Gimpel, K., Schneider, N., O’Connor, B., Das, D., Mills, D., Eisenstein, J., Heilman, M., Yogatama, D., Flanigan, J., Smith, N.A.: Part-of-speech tagging for twitter: annotation, features, and experiments. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: Short Papers, HLT 2011, vol. 2, pp. 42–47. Association for Computational Linguistics, Stroudsburg (2011)
Hong, L., Dan, O., Davison, B.D.: Predicting popular messages in twitter. In: Proceedings of the 20th International Conference Companion on World Wide Web, WWW 2011, pp. 57–58. ACM, New York (2011)
Jansen, B.J., Zhang, M., Sobel, K., Chowdury, A.: Twitter power: Tweets as electronic word of mouth. J. Am. Soc. Inf. Sci. Technol. 60(11), 2169–2188 (2009)
Joachims, T.: Making large scale svm learning practical (1999)
Kim, S.M., Pantel, P., Chklovski, T., Pennacchiotti, M.: Automatically assessing review helpfulness. In: Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, pp. 423–430. Association for Computational Linguistics (2006)
Liu, P., Tang, J., Wang, T.: Information current in twitter: which brings hot events to the world. In: Proceedings of the 22nd International Conference on World Wide Web Companion, pp. 111–112. International World Wide Web Conferences Steering Committee (2013)
Liu, T.Y.: Learning to rank for information retrieval. Found. Trends Inf. Retr. 3(3), 225–331 (2009)
Liu, Y., Huang, X., An, A., Yu, X.: Modeling and predicting the helpfulness of online reviews. In: ICDM, pp. 443–452 (2008)
Luo, Z., Osborne, M., Petrovic, S., Wang, T.: Improving twitter retrieval by exploiting structural information. In: AAAI 2012: Proceedings of the Twenty-Sixth AAAI (2012)
Luo, Z., Osborne, M., Tang, J., Wang, T.: Who will retweet me? finding retweeters in twitter. In: Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM (2013)
Luo, Z., Osborne, M., Wang, T.: Opinion retrieval in twitter. In: ICWSM (2012)
O’Connor, B., Balasubramanyan, R., Routledge, B.R., Smith, N.A.: From tweets to polls: Linking text sentiment to public opinion time series. In: ICWSM (2010)
Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retr. 2(1-2), 1–135 (2008)
Petrovic, S., Osborne, M., Lavrenko, V.: Rt to win! predicting message propagation in twitter. In: ICWSM (2011)
Stieglitz, S., Dang-Xuan, L.: Political communication and influence through microblogging-an empirical analysis of sentiment in twitter messages and retweet behavior. In: HICSS, pp. 3500–3509 (2012)
Zhang, W., Yu, C., Meng, W.: Opinion retrieval from blogs. In: Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management, CIKM 2007, pp. 831–840. ACM, New York (2007)
Zhang, Z., Varadarajan, B.: Utility scoring of product reviews. In: Proceedings of the 15th ACM International Conference on Information and Knowledge Management, pp. 51–57. ACM (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Luo, Z., Tang, J., Wang, T. (2013). Propagated Opinion Retrieval in Twitter. In: Lin, X., Manolopoulos, Y., Srivastava, D., Huang, G. (eds) Web Information Systems Engineering – WISE 2013. WISE 2013. Lecture Notes in Computer Science, vol 8181. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41154-0_2
Download citation
DOI: https://doi.org/10.1007/978-3-642-41154-0_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-41153-3
Online ISBN: 978-3-642-41154-0
eBook Packages: Computer ScienceComputer Science (R0)