Topical Keyphrase Extraction from Twitter

Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pages 379–388, Portland, Oregon, June 19-24, 2011. c⃝2011 Association for Computational Linguistics Topical Keyphrase Extraction from Twitter Wayne Xin Zhao† Jing Jiang‡ Jing He† Yang Song† Palakorn Achananuparp‡ Ee-Peng Lim‡ Xiaoming Li† †School of Electronics Engineering and Computer Science, Peking University ‡School of Information Systems, Singapore Management University {batmanfly,peaceful.he,songyangmagic}@gmail.com, {jingjiang,eplim,palakorna}@smu.edu.sg, lxm@pku.edu.cn Abstract Summarizing and analyzing Twitter content is an important and challenging task. In this pa- per, we propose to extract topical keyphrases as one way to summarize Twitter. We propose a context-sensitive topical PageRank method for keyword ranking and a probabilistic scor- ing function that considers both relevance and interestingness of keyphrases for keyphrase ranking. We evaluate our proposed methods on a large Twitter data set. Experiments show that these methods are very effective for topi- cal keyphrase extraction. 1 Introduction Twitter, a new microblogging website, has attracted hundreds of millions of users who publish short messages (a.k.a. tweets) on it. They either pub- lish original tweets or retweet (i.e. forward) oth- ers’ tweets if they find them interesting. Twitter has been shown to be useful in a number of appli- cations, including tweets as social sensors of real- time events (Sakaki et al., 2010), the sentiment pre- diction power of Twitter (Tumasjan et al., 2010), etc. However, current explorations are still in an early stage and our understanding of Twitter content still remains limited. How to automatically under- stand, extract and summarize useful Twitter content has therefore become an important and emergent re- search topic. In this paper, we propose to extract keyphrases as a way to summarize Twitter content. Tradition- ally, keyphrases are defined as a short list of terms to summarize the topics of a document (Turney, 2000). It can be used for various tasks such as document summarization (Litvak and Last, 2008) and index- ing (Li et al., 2004). While it appears natural to use keyphrases to summarize Twitter content, compared with traditional text collections, keyphrase extrac- tion from Twitter is more challenging in at least two aspects: 1) Tweets are much shorter than traditional articles and not all tweets contain useful informa- tion; 2) Topics tend to be more diverse in Twitter than in formal articles such as news reports. So far there is little work on keyword or keyphrase extraction from Twitter. Wu et al. (2010) proposed to automatically generate personalized tags for Twit- ter users. However, user-level tags may not be suit- able to summarize the overall Twitter content within a certain period and/or from a certain group of peo- ple such as people in the same region. Existing work on keyphrase extraction identifies keyphrases from either individual documents or an entire text collec- tion (Turney, 2000; Tomokiyo and Hurst, 2003). These approaches are not immediately applicable to Twitter because it does not make sense to ex- tract keyphrases from a single tweet, and if we ex- tract keyphrases from a whole tweet collection we will mix a diverse range …

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