A Fine-grained Sentiment Analysis Algorithm for Automotive Reviews

Chen Bing-feng; Hao Zhi-feng; Cai Rui-chu; Wen Wen; Wang Li-juan; Huang Hao; Cai Xiao-feng

doi:10.12052/gdutxb.170036

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A Fine-grained Sentiment Analysis Algorithm for Automotive Reviews

更新时间：2026-05-27

- A Fine-grained Sentiment Analysis Algorithm for Automotive Reviews
- Vol. 34, Issue 3, Pages: 8-14(2017)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.12052/gdutxb.170036
  CLC： TP181
- Online First：09 May 2017，
  
  Published：2017
- 稿件说明：
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陈炳丰, 郝志峰, 蔡瑞初, 温雯, 王丽娟, 黄浩, 蔡晓凤. 面向汽车评论的细粒度情感分析方法研究[J]. 广东工业大学学报, 2017, 34(3): 8-14.

Chen Bing-feng, Hao Zhi-feng, Cai Rui-chu, et al. A Fine-grained Sentiment Analysis Algorithm for Automotive Reviews[J]. 2017, 34(3): 8-14.
陈炳丰, 郝志峰, 蔡瑞初, 温雯, 王丽娟, 黄浩, 蔡晓凤. 面向汽车评论的细粒度情感分析方法研究[J]. 广东工业大学学报, 2017, 34(3): 8-14. DOI： 10.12052/gdutxb.170036.

Chen Bing-feng, Hao Zhi-feng, Cai Rui-chu, et al. A Fine-grained Sentiment Analysis Algorithm for Automotive Reviews[J]. 2017, 34(3): 8-14. DOI： 10.12052/gdutxb.170036.

摘要

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.

Abstract

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.

关键词

Keywords

references

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