›› 2015, Vol. 30 ›› Issue (5): 1120-1129.doi: 10.1007/s11390-015-1587-1

Special Issue: Artificial Intelligence and Pattern Recognition; Data Management and Data Mining

• Special Section on Social Media Processing • Previous Articles     Next Articles

Microblog Sentiment Analysis with Emoticon Space Model

Fei Jiang1,2,3(姜飞), Yi-Qun Liu1,2,3*(刘奕群), Senior Member, CCF, Huan-Bo Luan1,2,3(栾焕博)Jia-Shen Sun4(孙甲申), Xuan Zhu4(朱璇), Min Zhang1,2,3(张敏), Senior Member, CCF, Shao-Ping Ma1,2,3(马少平)   

  1. 1 State Key Laboratory of Intelligent Technology and Systems, Beijing 100084, China;
    2 Tsinghua National Laboratory for Information Science and Technology, Beijing 100084, China;
    3 Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China;
    4 Language Computing Laboratory, Samsung Research & Development Institute of China, Beijing 100028, China
  • Received:2014-11-15 Revised:2015-07-03 Online:2015-09-05 Published:2015-09-05
  • Contact: Yi-Qun Liu E-mail:yiqunliu@tsinghua.edu.cn
  • About author:Fei Jiang is a master candidate in Department of Computer Science and Technology, Tsinghua University, Beijing. He received his B.S. degree in computer science from Tsinghua University in 2013. His research interests include information retrieval and natural language processing.
  • Supported by:

    This work was supported by Tsinghua-Samsung Joint Laboratory, the National Basic Research 973 Program of China under Grant No. 2015CB358700, and the National Natural Science Foundation of China under Grant Nos. 61472206, 61073071, and 61303075.

Emoticons have been widely employed to express different types of moods, emotions, and feelings in microblog environments. They are therefore regarded as one of the most important signals for microblog sentiment analysis. Most existing studies use several emoticons that convey clear emotional meanings as noisy sentiment labels or similar sentiment indicators. However, in practical microblog environments, tens or even hundreds of emoticons are frequently adopted and all emoticons have their own unique emotional meanings. Besides, a considerable number of emoticons do not have clear emotional meanings. An improved sentiment analysis model should not overlook these phenomena. Instead of manually assigning sentiment labels to several emoticons that convey relatively clear meanings, we propose the emoticon space model (ESM) that leverages more emoticons to construct word representations from a massive amount of unlabeled data. By projecting words and microblog posts into an emoticon space, the proposed model helps identify subjectivity, polarity, and emotion in microblog environments. The experimental results for a public microblog benchmark corpus (NLP&CC 2013) indicate that ESM effectively leverages emoticon signals and outperforms previous state-of-the-art strategies and benchmark best runs.

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