Modeling Rich Contexts for Sentiment Classification with LSTM

TL;DR

This paper introduces a hierarchical LSTM model to incorporate rich social and conversational contexts for sentiment analysis on social media data, significantly improving classification accuracy.

Contribution

It presents a novel hierarchical LSTM approach to model diverse contexts in tweets, addressing the challenge of short, noisy social media texts.

Findings

Contexts improve sentiment classification accuracy

Hierarchical LSTM effectively models long-range dependencies

Significant performance gains over baseline methods

Abstract

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.