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WU Jinyuan, JI Junzhong, ZHAO Xuewu, WU Chensheng, DU Fanghua. Weight Calculation of Emotional Word Based on Feature Selection Technique[J]. Journal of Beijing University of Technology, 2016, 42(1): 142-151. DOI: 10.11936/bjutxb2015040085
Citation: WU Jinyuan, JI Junzhong, ZHAO Xuewu, WU Chensheng, DU Fanghua. Weight Calculation of Emotional Word Based on Feature Selection Technique[J]. Journal of Beijing University of Technology, 2016, 42(1): 142-151. DOI: 10.11936/bjutxb2015040085
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Weight Calculation of Emotional Word Based on Feature Selection Technique

  • 1. Beijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology,College of Computer Science and Technology,Beijing University of Technology, Beijing 100124, China;

  • 2. School of Software,Nanyang Normal University, Nanyang 473061, Henan, China;

  • 3. Beijing Institute Science and Technology Information, Beijing 100048, China

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  • Received Date: April 28, 2015
  • Available Online: January 10, 2023
    Graphical Abstract
    • Abstract
  • It is very important for the text sentiment analysis to build an emotional dictionary. However,most of current researches in this area focus on the words' polarity discrimination. Researchers rarely study the weight assignment of emotional words,and methods on this already existed mostly need to select benchmark words through artificial ways. Using artificial ways brings great difficulty in practical application. To solve this problem,an automatic weight calculation approach of emotional words based on feature selection technique was proposed. Firstly some related assumptions between the emotional weight of words and the emotional tendency of texts were proposed; Then, centered around sentiment classification,the properties of binary classification was combined to improve information gain( IG) and chi-squarec( CHI); Finally,the improved feature selection methods to calculate the weight of emotional words were usesd. Experimental results show that using the emotion dictionary with the calculated weights in text sentiment classification can greatly improve the classification accuracy.
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    • References (13)
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