Learning Sentence Representation for Emotion Classification on Microblogs

摘要：

　　This paper studies the emotion classification task on microblogs.Given a message, we classify its emotion as happy, sad, angry or surprise.Ex isting methods mostly use the bag-of-word representation or manually designed features to train supervised or distant supervision models.However, manufactur ing feature engines is time-consuming and not enough to capture the complex linguistic phenomena on microblogs.In this study, to overcome the above prob lems, we utilize pseudo-labeled data, which is extensively explored for distant su pervision learning and training language model in Twitter sentiment analysis, to learn the sentence representation through Deep Belief Network algorithm.Exper imental results in the supervised learning framework show that using the pseudo labeled data, the representation learned by Deep Belief Network outperforms the Principal Components Analysis based and Latent Dirichlet Allocation based rep resentations.By incorporating the Deep Belief Network based representation into basic features, the performance is further improved.

关键词： Emotion Classification Deep Belief Network Representation Learning Microblogs

作者: Duyu Tang Bing Qin Ting Liu Zhenghua Li

作者单位: Research Center for Social Computing and Information Retrieval, Harbin Institute of Technology, Harbin, China

会议类型: 国际会议

会议名称: Second CCF Conference,NLPCC2013(第二届自然语言处理与中文计算会议)

会议地点: 重庆

会议语种:英文

页码: 212-223

在线出版日期: 2013-11-15（万方平台首次上网日期，不代表论文的发表时间）