基于Word2Vec和CNN的产品评论细粒度情感分析模型  

A Fine-grained Sentiment Analysis Model for Product Reviews Based on Word2Vec and CNN

作　　者：蔡庆平[1] 马海群[2] Cai Qingping;Ma Haiqun(School of Information Management,Heilongjiang University,Harbin 150080;Research Center of Information Resource Management,Heilongjiang University,Harbin 150080)

机构地区：[1]黑龙江大学信息管理学院,哈尔滨150080 [2]黑龙江大学信息资源管理研究中心,哈尔滨150080

出　　处：《图书情报工作》2020年第6期49-58,共10页Library and Information Service

基　　金：国家社会科学基金重点项目"开放数据与数据安全的政策协同研究"(项目编号:15ATQ008);黑龙江省省属高等学校基本科研业务费基础研究项目"基于深度学习的产品评论信息情感分析研究"(项目编号:RWSKCX201809)研究成果之一。

摘　　要：[目的/意义]构建一种基于Word2Vec和CNN的产品评论细粒度情感分析模型。[方法/过程]首先使用Word2Vec从产品评论中构建产品特征词列表和噪声词表,其次借助噪声词表来进行产品评论特征词的提取,然后采用CNN对产品评论进行产品特征层面的细粒度情感分类,最后实现基于产品特征的产品评论聚类。[结果/结论]通过爬取京东商城华为手机评论对该模型进行训练和测试,结果表明,该模型能够有效实现产品评论的细粒度情感分析,可以有效地发现用户对产品特征的关注度和满意度。[Purpose/significance]To construct a fine-grained sentiment analysis model for product reviews based on Word2Vec and CNN.[Method/process]This paper firstly applied Word2vec to build product feature vocabulary and noise vocabulary based on product reviews,secondly extracted the feature words from product reviews by the noise vocabulary,then classified the product reviews according to product features sentiment,finally realized product reviews clustering based on product features.[Result/conclusion]The model was trained and tested by the reviews of Huawei mobile phone on JingDong Mall,the results showed that the model could effectively realize fine-grained sentiment analysis of product reviews and find out users focus and satisfaction on product features.

关 键 词：情感分析 产品评论 CNN Word2Vec 细粒度 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]