Chinese Micro-Blog Emotion Classification by Exploiting Linguistic Features and SVM\(^{\textit{perf}}\)

  • Hua XuEmail author
  • Fan Zhang
  • Jiushuo Wang
  • Weiwei Yang
Part of the Studies in Computational Intelligence book series (SCI, volume 639)


These years, micro-blog emotion mining becomes one of the research hotspots in social network data mining. Different from state of the art study, this paper presents a novel method for emotion classification , which is SVM \(^{\textit{perf}}\) based method combined with syntactic structure of Chinese micro-blogs. The classified emotion type includes Happiness, Anger, Disgust, Fear, Sadness and Surprise. For the proposed method, an emotional lexicon is constructed and linguistic features are extracted from micro-blog corpus firstly. Secondly, for the current feature space dimension is higher, Chi-square test is used to extract the high-frequency and high-class relevance keywords. At the same time, Pointwise Mutual Information (PMI) is used to pick the effective low frequency words in feature dimension reduction, which can reduce the computational complexity. Finally, SVM\(^{\textit{perf}}\) is applied for the emotion classification. In order to illustrate the effectiveness of the algorithm, LIBSVM and SVM-Light are used as the baseline. The data from Sina Micro-blog ( have been used as the experiment data. The experiment results demonstrate that all the above features contribute to emotion classification in micro-blogs, and the results validate the feasibility of the proposed approach. It also shows that SVM \(^{\textit{perf}}\) is an appropriate choice of classifier for emotion classification.


Micro-blog Text mining Emotion classification SVM\(^{\textit{perf}}\) 



Supported by National Basic Research Program of China (973 Program) (Grant No:2012CB316301), National Natural Science Foundation of China (Grant No: 61175110), and Chinese National Programs for High Technology Research and Development (863 Program) (Grant No. 2013AA013702).


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and TechnologyTsinghua UniversityBeijingChina

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