商品评论情感倾向性分析  

Sentiment analysis of commodity reviews

作　　者：李明[1] 胡吉霞 侯琳娜[1] 严峻[1] LI Ming;HU Jixia;HOU Linna;YAN Jun(School of Economics and Management,Xi’an University of Technology,Xi’an Shaanxi 710054,China)

机构地区：[1]西安理工大学经济与管理学院

出　　处：《计算机应用》2019年第S02期15-19,共5页journal of Computer Applications

摘　　要：针对粗粒度的商品评论情感分析不能详尽地提供用户喜好问题,提出一种基于支持向量机(SVM)结合点互信息(PMI)的细粒度商品评论情感分析方法。首先,使用卡方检验方法进行文本特征选择和降维;接着,对朴素贝叶斯、决策树、支持向量机(SVM)、K最邻近算法(K NN)四种常用情感分类方法进行比较,支持向量机(SVM)的召回率和精确率最高,均达到94.5%,所以使用支持向量机(SVM)对商品评论进行粗粒度的情感分析;然后,根据人工经验总结典型的商品属性,使用点互信息(PMI)方法对商品属性扩充;最后针,对扩充后的商品属性,在以上粗粒度的商品评论情感分析基础上,进行细粒度的情感分析及统计。细粒度的商品评论情感分析,可使厂家看到用户对产品属性的喜好,以及在产品设计、销售及服务中需要改进的方面。Aiming at the problem that coarse-grained commodity review sentiment analysis cannot provide the user preference in detail,a method of fine-grained commodity comment sentiment analysis based on Support Vector Machine(SVM)combined with Point Mutual Information(PMI)was proposed.Firstly,chi-square test method was used for text feature selection and dimensionality reduction.Then,four common sentiment classification methods,such as Naive Bayes,decision tree,Support Vector Machine(SVM)and K Nearest Neighbors(K NN)algorithm,were compared.SVM had the highest recall rate and accuracy rate of 94.5%,therefore,it was used to conduct coarse-grained sentiment analysis of commodity reviews.Then,based on manual experience to summarize typical commodity attributes,PMI was used to expand the product attributes.Finally,based on the expanded product attributes and the above-mentioned coarse-grained product review sentiment analysis,fine-grained sentiment analysis and statistics was performed.Fine-grained product reviews sentiment analysis allows manufacturers to observe user preferences for product attributes and areas for improvement in product design,sales,and service.

关 键 词：情感分析 特征选择 文本分类 机器学习 商品属性 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]