ZHANG Yangsen, ZHANG Yaorong, JIANG Yuru, et al., “Multi-feature-Based Subjective-Sentence Classification Method for Chinese Micro-blogs,” Chinese Journal of Electronics, vol. 26, no. 6, pp. 1111-1117, 2017, doi: 10.1049/cje.2017.09.006
Citation: ZHANG Yangsen, ZHANG Yaorong, JIANG Yuru, et al., “Multi-feature-Based Subjective-Sentence Classification Method for Chinese Micro-blogs,” Chinese Journal of Electronics, vol. 26, no. 6, pp. 1111-1117, 2017, doi: 10.1049/cje.2017.09.006

Multi-feature-Based Subjective-Sentence Classification Method for Chinese Micro-blogs

doi: 10.1049/cje.2017.09.006
Funds:  This work is supported by the National Natural Science Foundation of China (No.61370139, No.61602044).
  • Received Date: 2017-04-05
  • Rev Recd Date: 2017-05-18
  • Publish Date: 2017-11-10
  • The accurate classification of subjective and objective sentences is important in the preparation for micro-blog sentiment analysis. Since a single feature type cannot provide enough subjective information for classification, we propose a Support vector machine (SVM)-based classification model for Chinese micro-blogs using multiple features. We extracted the subjective features from the Part of speech (POS) and the dependency relationship between words, and constructed a 3-POS subjective pattern set and a dependency template set. We fused these two types of features and used an SVM-based model to classify Chinese micro-blog text. The experimental results showed that the performance of the classification model improved remarkably when using multiple features.
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