基于句法规则和HowNet的商品评论细粒度观点分析  

Fine-grained Opinion Analysis of Product Reviews Based on Syntactic Rules and HowNet

作　　者：韦婷婷 陈伟生 胡勇军 骆威 包先雨 WEI Tingting;CHEN Weisheng;HU Yongjun;LUO Wei;BAO Xianyu(School of Mathematics and Informatics,South China Agricultural University,Guangzhou,Guangdong 510642,China;School of Management,Guangzhou University,Guangzhou,Guangdong 510006,China;Shenzhen Academy of Inspection and Quarantine,Shenzhen,Guangdong 518001,China)

机构地区：[1]华南农业大学数学与信息学院,广东广州510642 [2]广州大学工商管理学院,广东广州510006 [3]深圳市检验检疫科学研究院,广东深圳518001

出　　处：《中文信息学报》2020年第3期88-98,共11页Journal of Chinese Information Processing

基　　金：国家自然科学基金(61702197);国家重点研发计划课题(2016YFF0202504);广东省社科“十三五”规划项目(GD17CGL01);华南农业大学大学生创新创业计划训练项目(201810564284)。

摘　　要：该文提出一种基于句法规则和HowNet词典的商品评论细粒度观点分析方法,主要包括三个模块:评价对象抽取、评价对象-评价词对抽取、评价对象总体观点得分计算。具体思路为:首先,结合词性标注和频繁项集方法构建一个初始的评价对象词典,便于重用和修正商品的总体评价维度;其次,基于爬取的电商评论文本真实数据设计了评价对象-评价词对抽取规则;最后,借助HowNet词典分别计算不同评价维度的观点综合得分,进而对比同一商品不同品牌在各个维度下的总体观点评价,该方法在商品评论语料集上验证了有效性。This paper proposes a fine-grained opinion analysis model based on syntactic rules and HowNet for product reviews. The model consists of three main modules: the target mining, the target-opinion mining, and the overall opinion estimation. Firstly, a target lexicon is constructed in terms of part-of-speech tagging and frequent item set mining, which is convenient to reuse and modify the overall opinion target of products. Secondly, rules are designed for target-opinion extraction based on the real data of e-commerce reviews. Finally, HowNet dictionary is adopted to estimate the overall score of all targets, and then to compare different brands of the same product in each evaluation perspective. The validity of this method is verified on product review corpus.

关 键 词：商品评论 细粒度观点分析 句法规则 HOWNET 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]