Abstract
Opinion retrieval deals with finding relevant documents that express either a negative or positive opinion about some topic. Social Networks such as Twitter, where people routinely post opinions about almost any topic, are rich environments for opinions. However, spam and wildly varying documents makes opinion retrieval within Twitter challenging. Here we demonstrate how we can exploit social and structural textual information of Tweets and improve Twitter-based opinion retrieval. In particular, within a learning-to-rank technique, we explore the question of whether aspects of an author (such as the number of friends they have), information derived from the body of Tweets and opinionatedness ratings of Tweets can improve performance. Experimental results show that social features can improve retrieval performance. Retrieval using a novel unsupervised opinionatedness feature achieves comparable performance with a supervised method using manually tagged Tweets. Topic-related specific structured Tweet sets are shown to help with query-dependent opinion retrieval. Finally, we further verify the effectiveness of our approach for opinion retrieval in re-tagged TREC Tweets2011 corpus.
Article PDF
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Avoid common mistakes on your manuscript.
References
Akritidis, L., Bozanis, P.: Improving opinionated blog retrieval effectiveness with quality measures and temporal features. World Wide Web, 1–22 (2013)
Amati, G., Ambrosi, E., Bianchi, M., Gaibisso, C., Gambosi, G.: Automatic construction of an opinion-term vocabulary for ad hoc retrieval. In: Proceedings of the IR research, 30th European conference on Advances in information retrieval, ECIR’08, pp 89–100. Springer-Verlag, Berlin, Heidelberg (2008)
Barbosa, L., Feng, J.: Robust sentiment detection on twitter from biased and noisy data. In: Proceedings of the 23rd International Conference on Computational Linguistics, Posters, COLING ’10, pp. 36–44. Association for Computational Linguistics, Stroudsburg (2010)
Bollen, J., Mao, H., Zeng, X.J.: Twitter mood predicts the stock market. J. Comput. Sci. 2(1), 1–8 (2011)
Davidov, D., Tsur, O., Rappoport, A.: Enhanced sentiment learning using twitter hashtags and smileys. In: Proceedings of the 23rd International Conference on Computational Linguistics: Posters, COLING ’10, pp. 241–249. Association for Computational Linguistics, Stroudsburg (2010)
Duan, Y., Jiang, L., Qin, T., Zhou, M., Shum, H.: An empirical study on learning to rank of tweets. In: Proceedings of the 23rd International Conference on Computational Linguistics, pp. 295–303. Association for Computational Linguistics (2010)
Efron, M.: Hashtag retrieval in a microblogging environment. In: Proceeding of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 787–788. ACM (2010)
Eguchi, K., Lavrenko, V.: Sentiment retrieval using generative models. In: Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, EMNLP ’06, pp. 345–354. Association for Computational Linguistics, Stroudsburg (2006)
Gerani, S., Carman, M., Crestani, F.: Aggregation methods for proximity-based opinion retrieval. ACM Trans. Inf. Syst. (TOIS) 30(4), 26 (2012)
Gerani, S., Carman, M.J., Crestani, F.: Investigating learning approaches for blog post opinion retrieval. In: Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval, ECIR ’09, pp. 313–324. Springer-Verlag, Berlin, Heidelberg (2009) doi:10.1007/978-3-642-00958-7_29
Gerani, S., Carman, M.J., Crestani, F.: Proximity-based opinion retrieval. In: Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 403–410. ACM (2010)
Gerani, S., Keikha, M., Carman, M., Crestani, F.: Personal blog retrieval using opinion features. In: Proceedings of the 33rd European Conference on Advances in Information Retrieval, ECIR’11, pp 747–750. Springer-Verlag, Berlin, Heidelberg (2011)
Go, A., Bhayani, R., Huang, L.: Twitter sentiment classification using distant supervision. Processing, pp. 1–6 (2009)
Gouws, S., Metzler, D., Cai, C., Hovy, E.: Contextual bearing on linguistic variation in social media. In: Proceedings of the Workshop on Languages in Social Media, LSM ’11, pp 20–29. Association for Computational Linguistics, Stroudsburg (2011)
He, B., Macdonald, C., He, J., Ounis, I.: An effective statistical approach to blog post opinion retrieval. In: Proceedings of the 17th ACM Conference on Information and Knowledge Management, CIKM ’08. pp. 1063–1072. ACM, New York (2008)
Huang, X., Croft, W.B.: A unified relevance model for opinion retrieval. In: Proceedings of the 18th ACM Conference on Information and Knowledge Management, CIKM ’09, pp 947–956. ACM, New York (2009)
Jansen, B.J., Zhang, M., Sobel, K., Chowdury, A.: Twitter power: Tweets as electronic word of mouth, pp. 2169–2188. Wiley, New York. (2009) doi:10.1002/asi.v60:11
Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, HLT ’11, vol. 1, pp. 151–160. Association for Computational Linguistics, Stroudsburg (2011)
Jijkoun, V., de Rijke, M., Weerkamp, W.: Generating focused topic-specific sentiment lexicons. In: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, ACL ’10, pp. 585–594. Association for Computational Linguistics, Stroudsburg (2010)
Korenek, P., Šimko, M.: Sentiment analysis on microblog utilizing appraisal theory. World Wide Web, pp. 1–21 (2013)
Li, B., Zhou, L., Feng, S., Wong, K.F.: A unified graph model for sentence-based opinion retrieval. In: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, ACL ’10, pp. 1367–1375. Association for Computational Linguistics, Stroudsburg (2010)
Liu, T.Y.: Learning to rank for information retrieval. Found. Trends Inf. Retr. 3(3), 225–331 (2009). doi:10.1561/1500000016
Luo, Z., Osborne, M., Petrovic, S., Wang, T.: Improving twitter retrieval by exploiting structural information. In: AAAI ’12: Proceedings of the Twenty-Sixth AAAI (2012)
Luo, Z., Osborne, M., Wang, T.: Opinion retrieval in twitter. In: 6th International AAAI Conference on Weblogs and Social Media (2012)
Macdonald, C., Ounis, I., Soboroff, I.: Overview of the trec 2007 blog track. In: TREC (2007)
Manning, C.D., Raghavan, P., Schtze, H.: Introduction to Information Retrieval. Cambridge University Press, New York (2008)
Massoudi, K., Tsagkias, M., de Rijke, M., Weerkamp, W.: Incorporating query expansion and quality indicators in searching microblog posts. Advances in Information Retrieval, pp. 362–367 (2011)
Metzler, D., Cai, C.: Usc/isi at trec 2011: Microblog track. In: TREC (2011)
Na, S.H., Lee, Y., Nam, S.H., Lee, J.H.: Improving opinion retrieval based on query-specific sentiment lexicon. In: Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval, ECIR ’09, pp. 734–738. Springer-Verlag, Berlin, Heidelberg (2009). doi:10.1007/978-3-642-00958-7_76
Naveed, N., Gottron, T., Kunegis, J., Alhadi, A.: Searching microblogs: coping with sparsity and document quality. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, pp. 183–188. ACM (2011)
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)
Orimaye, S.O., Alhashmi, S.M., Siew, E.G.: Can predicate-argument structures be used for contextual opinion retrieval from blogs? World Wide Web, pp. 1–29 (2012)
Ounis, I., Lin, J., Soboroff, I.: Overview of the trec-2011 microblog track. In: TREC (2011)
Ounis, I., Macdonald, C., de Rijke, M., Mishne, G., Soboroff, I.: Overview of the trec 2006 blog track. In: TREC (2006)
Ounis, I., Macdonald, C., Soboroff, I.: Overview of the trec 2008 blog track. In: TREC (2008)
Robertson, S., Walker, S., Jones, S., Hancock-Beaulieu, M., Gatford, M., et al.: Okapi at trec-3. NIST SPECIAL PUBLICATION SP, pp. 109–109 (1995)
Salton, G., Wong, A., Yang, C.: A vector space model for automatic indexing. Commun. ACM 18(11), 613–620 (1975)
Seki, K., Uehara, K.: Adaptive subjective triggers for opinionated document retrieval. In: Proceedings of the 2nd ACM International Conference on Web Search and Data Mining, WSDM ’09, pp. 25–33. ACM, New York (2009). doi:10.1145/1498759.1498805
Zhang, M., Ye, X.: A generation model to unify topic relevance and lexicon-based sentiment for opinion retrieval. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’08, pp. 411–418. ACM, New York (2008). doi:10.1145/1390334.1390405
Zhang, W., Yu, C., Meng, W.: Opinion retrieval from blogs. In: Proceedings of the 16th ACM Conference on Information and Knowledge Management, CIKM ’07, pp. 831–840. ACM, New York (2007). doi:10.1145/1321440.1321555
Author information
Authors and Affiliations
Corresponding author
Additional information
A preliminary version of this paper appears in the proceedings of the 6th International AAAI Conference on Weblogs and Social Media, Dublin, Ireland, 2012.
Rights and permissions
About this article
Cite this article
Luo, Z., Osborne, M. & Wang, T. An effective approach to tweets opinion retrieval. World Wide Web 18, 545–566 (2015). https://doi.org/10.1007/s11280-013-0268-7
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11280-013-0268-7