Context-Aware Chinese Microblog Sentiment Classification with Bidirectional LSTM

摘要：

　　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.

关键词： Context-aware sentiment Recurrent neural networks Bidirectional long short-term memory Sentiment classification

作者: Yang Wang Shi Feng Daling Wang Yifei Zhang Ge Yu

作者单位: School of Computer Science and Engineering,Northeastern University,Shenyang,China School of Computer Science and Engineering,Northeastern University,Shenyang,China;Key Laboratory of

会议类型: 国际会议

会议名称: International Asia-Pacific Web Conference(第18届国际亚太互联网大会)

会议地点: 苏州

会议语种:英文

页码: 594-606

在线出版日期: 2016-09-23（万方平台首次上网日期，不代表论文的发表时间）