Topic-dependent sentiment classification on Twitter

Abstract

In this paper, we investigate how discovering the topic dicussed in a tweet can be used to improve its sentiment classification. In particular, a classifier is introduced consisting of a topic-specific classifier, which is only trained on tweets of the same topic of the given tweet, and a generic classifier, which is trained on all the tweets in the training set. The set of considered topics is obtained by clustering the hashtags that occur in the training set. A classifier is then used to estimate the topic of a previously unseen tweet. Experimental results based on a public Twitter dataset show that considering topic-specific sentiment classifiers indeed leads to an improvement.