A feature selection method based on improved fisher's discriminant ratio for text sentiment classification
A feature selection method based on improved fisher's discriminant ratio for text sentiment classification
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DOI：
10.1016/j.eswa.2011.01.077
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发表时间：
2011-07
期刊：
影响因子：
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通讯作者：
Suge Wang;Deyu Li;Xiaolei Song;Yingjie Wei;Hongxia Li
中科院分区：
文献类型：
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作者：
Suge Wang;Deyu Li;Xiaolei Song;Yingjie Wei;Hongxia Li
互联网以其开放性、虚拟性和共享性的特点，迅速成为人们表达意见、态度、情感和情绪的平台。由于主观性文本往往太多，人们难以浏览，如何自动将其分为不同的情感倾向类别（如积极/消极）成为一个重要的研究问题。针对主观文本情感分类问题，提出了一种基于Fisher判别比的有效特征选择方法。为了验证该方法的有效性，在采用支持向量机作为分类器的情况下，将其与基于信息增益的方法进行了比较。通过两种不同的特征选择方法与两种候选特征集相结合，进行了两个实验。在COAE 2008的2739个主观性文档和1006个汽车相关主观性文档下的实验结果表明，当候选特征为同时出现在正面和负面文本中的词时，基于词频估计的Fisher判别比在两个语料库下的性能最好，准确率分别为86.61%和82.80%.
Owing to its openness, virtualization and sharing criterion, the Internet has been rapidly becoming a platform for people to express their opinion, attitude, feeling and emotion. As the subjectivity texts are often too many for people to go through, how to automatically classify them into different sentiment orientation categories (e.g. positive/negative) has become an important research problem. In this paper, based on Fisher’s discriminant ratio, an effective feature selection method is proposed for subjectivity text sentiment classification. In order to validate the proposed method, we compared it with the method based on Information Gain while Support Vector Machine is adopted as the classifier. Two experiments are conducted by combining different feature selection methods with two kinds of candidate feature sets. Under 2739 subjectivity documents of COAE2008s and 1006 car-related subjectivity documents, the experimental results indicate that the Fisher’s discriminant ratio based on word frequency estimation has the best performance respectively with accuracy 86.61% and 82.80% under two corpus while the candidate features are the words which appear in both positive and negative texts.