基于产品特征树和LSTM模型的产品评论情感分析

Sentiment Analysis of Product Reviews Based on Product Feature Tree and LSTM Model

[目的/意义]从产品评论数据挖掘角度出发,研究多层次、细粒度评论倾向性分析问题,为企业提供更全面的产品改善意见.[方法/过程]提出一种基于产品特征树和LSTM模型的产品评论情感分析方法.该方法结合行业产品特点和依存句法分析结果,通过特征类别、层级和特征表述词构建产品特征树;在此基础上,根据用户评论分句及其所包含的产品特征词汇,运用深度学习LSTM模型进行评论分句情感识别和产品特征情感分布计算.[结果/结论]利用真实汽车产品评论数据集进行试验检验,结果表明该方法情感分类准确率高,可实现面向产品特征层级的多粒度情感分布测算.[局限]产品特征树构建需要人工参与,方法模型的普适性有待进一步检验.

[Purpose/significance]From the perspective of product reviews data mining,the paper focuses on multi-level and fine-grained tendency analysis to provide enterprises with more comprehensive opinions for products improvement.[Method/process]A method of sentiment analysis of product reviews based on product feature tree and LSTM model is proposed.The method combines industry product characteristics and dependency syntactic parsing results to construct product feature tree through feature categories,hierarchies and feature expressions.On this basis,according to the review text clauses and the product feature terms,the paper utilizes the LSTM model of deep learning to recognize emotion tendencies of review clauses and calculates emotion distribution of product features.[Result/conclusion]The experimental results show that the method has high accuracy in sentiment classification of review text clauses and can realize multi-granularity emotion evaluation on levels of product features.[Limitations]The construction of product feature tree recpiires manual participation,and the universality of the method model needs to be further tested.