JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2014, Vol. 49 ›› Issue (11): 74-81.doi: 10.6040/j.issn.1671-9352.3.2014.328

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Feature selection algorithm based on sentiment topic model

ZHENG Yan1, PANG Lin2, BI Hui2, LIU Wei2, CHENG Gong2   

  1. 1. Beijing Founder Electronics CO., Ltd, Beijing 100085, China;
    2. National Computer Network Emergency Response Technical Team Coordination Center of China, Beijing 100083, China
  • Received:2014-08-28 Revised:2014-10-17 Online:2014-11-20 Published:2014-11-25

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Abstract: In order to exert potential commercial value and social value of subjectivity text in enterprise business intelligence and public opinion survey and so on, a novel feature selection algorithm based on sentiment topic model was proposed, which takes both opinion term and opinion co-occurrence term into consideration to help topic modeling, and then the conditional distributions of opinion term in positive topic and negative topic were effectively estimated. This method tries to measure the importance of opinion feature in sentiment orientation. SVM was used in the experimental stage for classification.The experiment result shows that the algorithm has a higher recognition ratio and offers practical capabilities for cross-domain.

Key words: text classification, feature selection, opinion mining, topic model

CLC Number: 

  • TP391
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