Context-Aware Chinese Microblog Sentiment Classification with Bidirectional LSTM

Abstract

Recently, with the fast development of the microblog, analyzing the sentiment orientations of the tweets has become a hot research topic for both academic and industrial communities. Most of the existing methods treat each microblog as an independent training instance. However, the sentiments embedded in tweets are usually ambiguous and context-aware. Even a non-sentiment word might convey a clear emotional tendency in the microblog conversations. In this paper, we regard the microblog conversation as sequence, and leverage bidirectional Long Short-Term Memory (BLSTM) models to incorporate preceding tweets for context-aware sentiment classification. Our proposed method could not only alleviate the sparsity problem in the feature space, but also capture the long distance sentiment dependency in the microblog conversations. Extensive experiments on a benchmark dataset show that the bidirectional LSTM models with context information could outperform other strong baseline algorithms.