Abstract
Twitter plays a significant role in information diffusion and has evolved to an important information resource as well as news feed. People wonder and care about what is happening on Twitter and what news it is bringing to us every moment. However, with huge amount of data, it is impossible to tell what topic is trending on time manually, which makes real-time topic detection attractive and significant. Furthermore, Twitter provides a platform of opinion sharing and sentiment expression for events, news, products etc. Users intend to tell what they are really thinking about on Twitter thus makes Twitter a valuable source of opinions. Nevertheless, most works about trending topic detection fail to take sentiment into consideration. This work is based on a non-parametric supervised real-time trending topic detection model with sentimental feature. Experiment shows our model successfully detects trending sentimental topic in the shortest time. After a combination of multiple features, e.g. tweet volume and user volume, it demonstrates impressive effectiveness with 82.3% recall and surpasses all the competitors.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Agarwal, A., Xie, B., Vovsha, I., Rambow, O., Passonneau, R.: Sentiment analysis of twitter data. In: LSM, pp. 30–38. Association for Computational Linguistics (2011)
Barbosa, L., Feng, J.: Robust sentiment detection on twitter from biased and noisy data. In: COLING(Poster), pp. 36–44. Association for Computational Linguistics (2010)
Becker, H., Naaman, M., Gravano, L.: Beyond trending topics: Real-world event identification on twitter. In: ICWSM (2011)
Cataldi, M., Di Caro, L., Schifanella, C.: Emerging topic detection on twitter based on temporal and social terms evaluation. In: MDMKDD, p. 4. ACM (2010)
Diao, Q., Jiang, J., Zhu, F., Lim, E.P.: Finding bursty topics from microblogs. In: ACL, pp. 536–544. Association for Computational Linguistics (2012)
Gao, Z.J., Song, Y., Liu, S., Wang, H., Wei, H., Chen, Y., Cui, W.: Tracking and connecting topics via incremental hierarchical dirichlet processes. In: ICDM, pp. 1056–1061. IEEE (2011)
Go, A., Bhayani, R., Huang, L.: Twitter sentiment classification using distant supervision. CS224N Project Report, Stanford, pp. 1–12 (2009)
Joachims, T.: Svmlight: Support vector machine. SVM-Light Support Vector Machine http://svmlight. joachims. org/, University of Dortmund 19(4) (1999)
Kwak, H., Lee, C., Park, H., Moon, S.: What is twitter, a social network or a news media? In: WWW, pp. 591–600. ACM (2010)
Li, B., Zhou, L., Wei, Z., Wong, K., Xu, R., Xia, Y.: Web information mining and decision support platform for the modern service industry. In: ACL, pp. 97–102 (2014), http://aclweb.org/anthology/P/P14/P14-5017.pdf
Liu, B.: Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies 5(1), 1–167 (2012)
Mathioudakis, M., Koudas, N.: Twittermonitor: Trend detection over the twitter stream. In: SIGMOD, pp. 1155–1158. ACM (2010)
Nikolov, S.: Trend or No Trend: A Novel Nonparametric Method for Classifying Time Series. Ph.D. thesis, Massachusetts Institute of Technology (2012)
Ou, G., Chen, W., Wang, T., Wei, Z., Li, B., Yang, D., Wong, K.: Exploiting community emotion for microblog event detection. In: EMNLP, pp. 1159–1168 (2014)
Pang, B., Lee, L.: A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. In: ACL, p. 271. ACL (2004)
Pang, B., Lee, L.: Using very simple statistics for review search: An exploration. In: COLING (Posters), pp. 75–78 (2008)
Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up?: Sentiment classification using machine learning techniques. In: ACL, pp. 79–86. Association for Computational Linguistics (2002)
Riloff, E., Wiebe, J., Wilson, T.: Learning subjective nouns using extraction pattern bootstrapping. In: HLT-NAACL, pp. 25–32. Association for Computational Linguistics (2003)
Sakaki, T., Okazaki, M., Matsuo, Y.: Earthquake shakes twitter users: Real-time event detection by social sensors. In: WWW, pp. 851–860. ACM (2010)
Wang, X., Wei, F., Liu, X., Zhou, M., Zhang, M.: Topic sentiment analysis in twitter: a graph-based hashtag sentiment classification approach. In: CIKM, pp. 1031–1040. ACM (2011)
Wei, Z., Chen, J., Gao, W., Li, B., Zhou, L., He, Y., Wong, K.: An empirical study on uncertainty identification in social media context. In: ACL, pp. 58–62 (2013)
Wiebe, J., Riloff, E.: Creating subjective and objective sentence classifiers from unannotated texts. In: Gelbukh, A. (ed.) CICLing 2005. LNCS, vol. 3406, pp. 486–497. Springer, Heidelberg (2005)
Wiebe, J.M., Bruce, R.F., O’Hara, T.P.: Development and use of a gold-standard data set for subjectivity classifications. In: ACL, pp. 246–253. Association for Computational Linguistics (1999)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Peng, B., Li, J., Chen, J., Han, X., Xu, R., Wong, KF. (2015). Trending Sentiment-Topic Detection on Twitter. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2015. Lecture Notes in Computer Science(), vol 9042. Springer, Cham. https://doi.org/10.1007/978-3-319-18117-2_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-18117-2_5
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-18116-5
Online ISBN: 978-3-319-18117-2
eBook Packages: Computer ScienceComputer Science (R0)