JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2014, Vol. 49 ›› Issue (11): 22-30.doi: 10.6040/j.issn.1671-9352.3.2014.074

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Micro-blog opinion analysis based on syntactic dependency and feature combination

XIA Meng-nan, DU Yong-ping, ZUO Ben-xin   

  1. College of Computer Science, Beijing University of Technology, Beijing 100124, China
  • Received:2014-08-28 Revised:2014-10-17 Online:2014-11-20 Published:2014-11-25

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Abstract: Micro-blog opinion mining faces the difficulty because of the short text's conciseness. The technique of syntactic dependency relation analysis and CRFs(Conditional Random Fields) were combined to extract the candidate opinion objects. And then the dictionaries of the opinion analysis and all kinds of semantic features were used in the machine learning method to improve the performance of the opinion classification. The precision, recall and F1 values were used as the evaluation metric. The experimental results on the COAE(Chinese opinion analysis evaluation) data set verify both the validity of emotion factor extraction approach and the impact on opinion classification performance by different features. The macro and micro precisions for the opinion classification task are both 91.4%.

Key words: opinion mining, emotion factor extraction, feature selection

CLC Number: 

  • TP391
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