Open AccessMulti‐feature‐Based Subjective‐Sentence Classification Method for Chinese Micro‐blogs
Author(s) -
Zhang Yangsen,
Zhang Yaorong,
Jiang Yuru,
Huang Gaijuan
Publication year - 2017
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2017.09.006
Subject(s) - sentence , feature (linguistics) , computer science , artificial intelligence , pattern recognition (psychology) , natural language processing , linguistics , philosophy
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.