基于联合法选取特征的产品评论情感分类研究

Research on sentiment classification of product reviews based on combined method selecting features

随着互联网中信息资源不断膨胀,如何从海量的产品评论文本信息中获得对某一产品真实的、客观的评价,已成为一个亟待解决的问题。针对传统特征量选择方法的不足,该文采用了联合特征选取的方法,并构建了基于支持向量机的分类器,有效地实现了对产品评论文本情感倾向性的分类。对网络中获取的大量评论信息进行分析和研究,结果表明,提出的方法有效地克服了低频词中噪音词的干扰,提高了文本表示质量,改善了分类的效果。

With the ceaseless inflation of information resources on the Internet,how to get a real and objective evaluation about a product from huge amounts of product reviews information has becoming an urgent problem which should be solved.Aiming at the shortages of traditional feature selection methods,this paper has adopted combined method to select features,and constructed classifier based on SVM,which realized the emotion tendentiousness classification of product reviews texts effectively.Through analysis and research on massive comment information obtained from the Internet,it turns out that the method proposed in this paper conquers noise words’disturbance existing in low frequency words with effect,enhanced the quality of text representation,and improved the result of classification.