NDLI: Topic sentiment analysis in twitter: a graph-based hashtag sentiment classification approach

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Topic sentiment analysis in twitter: a graph-based hashtag sentiment classification approach

Content Provider	ACM Digital Library
Author	Liu, Xiaohua Wang, Xiaolong Zhang, Ming Wei, Furu Zhou, Ming
Abstract	Twitter is one of the biggest platforms where massive instant messages (i.e. tweets) are published every day. Users tend to express their real feelings freely in Twitter, which makes it an ideal source for capturing the opinions towards various interesting topics, such as brands, products or celebrities, etc. Naturally, people may anticipate an approach to receiving the common sentiment tendency towards these topics directly rather than through reading the huge amount of tweets about them. On the other side, Hashtags, starting with a symbol "#" ahead of keywords or phrases, are widely used in tweets as coarse-grained topics. In this paper, instead of presenting the sentiment polarity of each tweet relevant to the topic, we focus our study on hashtag-level sentiment classification. This task aims to automatically generate the overall sentiment polarity for a given hashtag in a certain time period, which markedly differs from the conventional sentence-level and document-level sentiment analysis. Our investigation illustrates that three types of information is useful to address the task, including (1) sentiment polarity of tweets containing the hashtag; (2) hashtags co-occurrence relationship and (3) the literal meaning of hashtags. Consequently, in order to incorporate the first two types of information into a classification framework where hashtags can be classified collectively, we propose a novel graph model and investigate three approximate collective classification algorithms for inference. Going one step further, we show that the performance can be remarkably improved using an enhanced boosting classification setting in which we employ the literal meaning of hashtags as semi-supervised information. Experimental results on a real-life data set consisting of 29,195 tweets and 2,181 hashtags show the effectiveness of the proposed model and algorithms.
Starting Page	1031
Ending Page	1040
Page Count	10
File Format	PDF
ISBN	9781450307178
DOI	10.1145/2063576.2063726
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2011-10-24
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Hashtag Sentiment analysis Graph-based classification Twitter
Content Type	Text
Resource Type	Article

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