教材在线评论的情感倾向性分析  

Emotional Tendency Analysis of Online Comments on Teaching Materials

作　　者：刘若兰[1] 年梅[1] 范祖奎[2] LIU Ruo-Lan NIAN Mei FAN Zu-Kui(The Computer Science＆Technolgy Department, Xinjiang Normal University, Urumqi 830054, China The Language Department, Xinjiang Police College, Urumqi 830011, China)

机构地区：[1]新疆师范大学计算机科学技术学院,乌鲁木齐830054 [2]新疆警察学院语言系,乌鲁木齐830011

出　　处：《计算机系统应用》2017年第10期144-149,共6页Computer Systems & Applications

基　　金：国家自然科学基金(61163064);教育部人文社会科学工程科技人才培养专项(15JDGC022);新疆师范大学数据安全重点实验室资助项目;新疆师范大学计算机应用技术重点学科资助

摘　　要：为了充分挖掘和应用电子商务网站中的教材评论信息,运用细粒度的情感分类算法对用户的在线评论进行分析,基于教材特征级的情感分析结果,辅助潜在客户和商家做出合理有效的决策.本文首先使用爬虫采集教材的在线评论文本,对其进行去噪、分词和词性标注等预处理;然后分析产品特征,在通用情感词典的基础上扩建领域情感词典;最后基于句法分析结果,结合教材评论的语言特性,设计适合教材评论的情感倾向性分析算法,并通过实验验证了算法的有效性.In order to fully tap and apply the information of textbook reviews on the e-commerce website, we use finegrained emotional classification algorithm to analyze the user＇s online comments, based on the sentiment analysis results of product feature level, so as to assist customers and businesses to make reasonable and effective decision. In this article,we first use the crawler tool to collect online comment texts of teaching materials, and carry on some pretreatments such as denoising, segmentation and POS tagging, and then analyze the product features, based on the general emotional dictionary expands domain sentiment dictionary. Finally, based on the syntactic analysis results, combined with the language features of textbook comments, we design an affective tendency analysis algorithm which is suitable for the textbook reviews, and prove the validity of the algorithm through experiments.

关 键 词：教材在线评论 细粒度情感分析 情感词典 产品特征 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]