面向汽车评论的细粒度情感分析方法研究

摘要

Sentiment analysis method can mine valuable information from a mass of automotive reviews, which has great application value in automotive product design and brand marketing. For the requirements of fine-grained analysis, a fine-grained sentiment analysis algorithm is put forward based on the entity. Firstly, the automotive reviews are preprocessed, then the model of Linear-chain CRF is used to do sentiment entity recognition and sentiment classification. Secondly, in order to relate the entity recognition with sentiment classification, the model of Linear-chain CRF is improved, and a method of two-level CRF proposed. Experimental results show that two-level CRF is better than Linear-chain CRF in sentiment analysis, which can meet the demand of fine-grained sentiment analysis of automotive reviews.%情感分析方法能够在海量的汽车评论信息中挖掘出有价值的信息,在汽车产品设计、品牌营销等方面具有较大的应用价值.针对汽车评论分析的细粒度分析要求,本文提出了基于实体的细粒度情感分析方法.首先,对汽车评论数据进行文本细粒度处理,然后采用Linear-chain CRF模型对评论数据进行情感实体识别和情感倾向分类;再对Linear-chain CRF模型进行改进,提出了一种构造双层结构的CRF模型的方法,解决2个任务间的关联问题.实验结果表明,双层结构CRF模型的情感分析效果优于Linear-chain CRF模型,能够满足汽车评论在情感实体识别与情感倾向分类的需求.