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Sentiment analysis on social media data such as tweets and weibo has become avery important and challenging task. Due to the intrinsic properties of suchdata, tweets are short, noisy, and of divergent topics, and sentimentclassification on these data requires to modeling various contexts such as theretweet/reply history of a tweet, and the social context about authors andrelationships. While few prior study has approached the issue of modelingcontexts in tweet, this paper proposes to use a hierarchical LSTM to model richcontexts in tweet, particularly long-range context. Experimental results showthat contexts can help us to perform sentiment classification remarkablybetter.

Modeling Rich Contexts for Sentiment Classification with LSTM