Multi-label Chinese Microblog Emotion Classification via Convolutional Neural Network

摘要：

　　Recently,analyzing peoples sentiments in microblogs has attracted more and more attentions from both academic and industrial communities.The traditional methods usually treat the sentiment analysis as a kind of single-label supervised learning problem that classifies the microblog according to sentiment orientation or single-labeled emotion.However,in fact multiple fine-grained emotions may be coexisting in just one tweet or even one sentence of the microblog.In this paper,we regard the emotion detection in microblogs as a multi-label classification problem.We leverage the skip-gram language model to learn distributed word representations as input features,and utilize a Convolutional Neural Network (CNN) based method to solve multi-label emotion classification problem in the Chinese microblog sentences without any manually designed features.Extensive experiments are conducted on two public short text datasets.The experimental results demonstrate that the proposed method outperforms strong baselines by a large margin and achieves excellent performance in terms of multi-label classification metrics.

关键词： Sentiment analysis Emotion classification Multi-label classification Convolutional Neural Network

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

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: 567-580

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）