ABSTRACT
Since the introduction of microblogging services, there has been a continuous growth of short-text social networking on the Internet. With the generation of large amounts of microposts, there is a need for effective categorization and search of the data. Twitter, one of the largest microblogging sites, allows users to make use of hashtags to categorize their posts. However, the majority of tweets do not contain tags, which hinders the quality of the search results. In this paper, we propose a novel method for unsupervised and content-based hashtag recommendation for tweets. Our approach relies on Latent Dirichlet Allocation (LDA) to model the underlying topic assignment of language classified tweets. The advantage of our approach is the use of a topic distribution to recommend general hashtags.
- S. M. Kywe, T.-A. Hoang, E.-P. Lim, and F. Zhu, "On Recommending Hashtags in Twitter Networks," in The 4th Int. Conference on Social Informatics, 2012. Google ScholarDigital Library
- E. Zangerle, W. Gassler, and G. Specht, "Recommending#-tags in twitter," in Proceedings of the Workshop on Semantic Adaptive Social Web, 2011.Google Scholar
- T. Li, Y. Wu, and Y. Zhang, "Twitter hash tag prediction algorithm," in ICOMP'11 - The 2011 International Conference on Internet Computing, 2011.Google Scholar
- A. Mazzia and J. Juett, "Suggesting Hashtags on Twitter," tech. rep., Computer Science and Engineering, University of Michigan, 2009.Google Scholar
- K. Gimpel, N. Schneider, B. O'Connor, D. Das, D. Mills, J. Eisenstein, M. Heilman, D. Yogatama, J. Flanigan, and N. A. Smith, "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 - Volume 2, HLT '11, (Stroudsburg, PA, USA), pp. 42--47, Association for Computational Linguistics, 2011. Google ScholarDigital Library
- T. Baldwin and M. Lui, "Language identification: the long and the short of the matter," in The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, 2010. Google ScholarDigital Library
- A. Dempster, N. Laird, and D. Rubin, "Maximum likelihood from incomplete data via the em algorithm," Journal of the Royal Statistical Society. Series B (Methodological), 1977.Google Scholar
- G. Heinrich, "Parameter estimation for text analysis," tech. rep., 2004.Google Scholar
Index Terms
- Using topic models for Twitter hashtag recommendation
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