Target-dependent Twitter Sentiment Classification

Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pages 151–160, Portland, Oregon, June 19-24, 2011. c⃝2011 Association for Computational Linguistics Target-dependent Twitter Sentiment Classification Long Jiang1 Mo Yu2 Ming Zhou1 Xiaohua Liu1 Tiejun Zhao2 1 Microsoft Research Asia 2 School of Computer Science & Technology Beijing, China Harbin Institute of Technology Harbin, China {longj,mingzhou,xiaoliu}@microsoft.com {yumo,tjzhao}@mtlab.hit.edu.cn Abstract Sentiment analysis on Twitter data has attract- ed much attention recently. In this paper, we focus on target-dependent Twitter sentiment classification; namely, given a query, we clas- sify the sentiments of the tweets as positive, negative or neutral according to whether they contain positive, negative or neutral senti- ments about that query. Here the query serves as the target of the sentiments. The state-of- the-art approaches for solving this problem always adopt the target-independent strategy, which may assign irrelevant sentiments to the given target. Moreover, the state-of-the-art approaches only take the tweet to be classified into consideration when classifying the senti- ment; they ignore its context (i.e., related tweets). However, because tweets are usually short and more ambiguous, sometimes it is not enough to consider only the current tweet for sentiment classification. In this paper, we pro- pose to improve target-dependent Twitter sen- timent classification by 1) incorporating target-dependent features; and 2) taking relat- ed tweets into consideration. According to the experimental results, our approach greatly im- proves the performance of target-dependent sentiment classification. 1 Introduction Twitter, as a micro-blogging system, allows users to publish tweets of up to 140 characters in length to tell others what they are doing, what they are thinking, or what is happening around them. Over the past few years, Twitter has become very popu- lar. According to the latest Twitter entry in Wik- ipedia, the number of Twitter users has climbed to 190 million and the number of tweets published on Twitter every day is over 65 million1. As a result of the rapidly increasing number of tweets, mining people’s sentiments expressed in tweets has attracted more and more attention. In fact, there are already many web sites built on the Internet providing a Twitter sentiment search ser- vice, such as Tweetfeel2, Twendz3, and Twitter Sentiment4. In those web sites, the user can input a sentiment target as a query, and search for tweets containing positive or negative sentiments towards the target. The problem needing to be addressed can be formally named as Target-dependent Sen- timent Classification of Tweets; namely, given a query, classifying the sentiments of the tweets as positive, negative or neutral according to whether they contain positive, negative or neutral senti- ments about that query. Here the query serves as the target of the sentiments. The state-of-the-art approaches for solving this problem, such as (Go et al., 20095; Barbosa and Feng, 2010), basically follow (Pang et al., 2002), who utilize machine learning based classifiers for the sentiment classification of texts. However, their classifiers actually work in a target-independent way: all the features used in the classifiers are in- dependent of the …

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