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