Modeling Rich Contexts for Sentiment Classification with LSTM
This addresses sentiment analysis for social media users, but it is incremental as it builds on existing LSTM methods for a known bottleneck in context modeling.
The paper tackled sentiment classification on noisy, short social media texts by modeling rich contexts like retweet history and social relationships using a hierarchical LSTM, resulting in significantly improved performance.
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.