面向汽车评论的细粒度情感分析方法研究

陈炳丰; 郝志峰; 蔡瑞初; 温雯; 王丽娟; 黄浩; 蔡晓凤

doi:10.12052/gdutxb.170036

欢迎来到能源电力期刊网！

English Version 期刊登录

您当前的位置：

首页 >

文章列表页 >

面向汽车评论的细粒度情感分析方法研究

大数据基础理论与应用专题 | 更新时间：2026-05-27

- 面向汽车评论的细粒度情感分析方法研究
- A Fine-grained Sentiment Analysis Algorithm for Automotive Reviews
- 广东工业大学学报 2017年34卷第3期页码：8-14
- 作者机构：
- 作者简介：
  
  陈炳丰(1983-),男,助理研究员,博士研究生,主要研究方向为数据挖掘、自然语言处理.E-mail:735180@qq.com
- 基金信息：
  
  国家自然科学基金资助项目（U1501254，61472089，61572143）;广东省自然科学基金资助项目（2014A030308008）;广东省自然科学杰出青年基金资助项目（2014A030306004）;广东省科技计划项目（2015B010108006）;广东省教育厅项目（2015KQNCX027）
- DOI：10.12052/gdutxb.170036
  中图分类号： TP181
- 网络首发：2017-05-09，
  
  纸质出版：2017
- 稿件说明：
移动端阅览
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

RANA T A, CHEAH Y. Aspect extraction in sentiment analysis:comparative analysis and survey[J]. Artificial Intelligence Review, 2016, 46(4):459-483.

RAVI K, RAVI V. A survey on opinion mining and sentiment analysis:Tasks, approaches and applications[J]. Knowledge-based Systems, 2015, 89:14-46.

赵妍妍, 秦兵, 刘挺. 文本情感分析[J]. 软件学报. 2010, 21(8):1834-1848. ZHAO Y Y, QIN B, LIU T. Sentiment analysis[J]. Journal of Software, 2010, 21(8):1834-1848.

李纲, 程明结, 寇广增. 基于情感倾向识别的汽车评论挖掘系统构建[J]. 情报学报. 2011, 30(2):204-211. LI G, CHENG M J, KOU G Z. The construction of car comments mining system based on sentiment analysis[J]. Journal of the China Society for Scientific and Technical Information. 2011, 30(2):204-211.

张晶, 李德玉, 王素格. 基于多标记学习的汽车评论文本多性能识别[J]. 计算机工程与科学. 2016, 38(1):188-194. ZHANG J, LI D Y, WANG S G. Multiple performances identification for car review texts based on multi-label learning[J]. Computer Engineering and Science. 2016, 38(1):188-194.

廖健, 王素格, 李德玉, 等. 基于观点袋模型的汽车评论情感极性分类[J]. 中文信息学报. 2015, 29(03):113-120. LIAO J, WANG S G, LI D Y, et al. The Bag-of-Opinions method for car review sentiment polarity classification[J]. Journal of Chinese Information Processing. 2015, 29(03):113-120.

王山雨. 面向产品领域的细粒度情感分析技术[D]. 哈尔滨:哈尔滨工业大学计算机科学与技术学院, 2011.

GATTI L, GUERINI M, TURCHI M. Sentiwords:Deriving a high precision and high coverage lexicon for sentiment analysis[J]. Ieee Transactions on Affective Computing. 2016, 7(4):409-421.

DEVARAJ M, PIRYANI R, SINGH V K. Lexicon ensemble and lexicon pooling for sentiment polarity detection[J]. IETE Technical Review. 2016, 33(3):332-340.

CHEN B, HAO Z, CAI R, et al. Sentiment target extraction based on CRFs with multi-features for Chinese microblog[C]//MORISHIMA A. Web Technologies and Applications-APWeb 2016. Heidelberg:Springer, 2016:29-41.

ZHANG S, LIU H, YANG L, et al. A cross-domain sentiment classification method based on extraction of key sentiment sentence[C]//LI J, JI H, ZHAO D, et al. Natural Language Processing and Chinese Computing. Nanchang:LNAI, 2015:9362, 90-101.

郑敏洁, 雷志城, 廖祥文, 等. 基于层叠CRFs的中文句子评价对象抽取[J]. 中文信息学报. 2013, 27(3):69-76. ZHENG M J, LEI Z C, LIAO X W, et al. Identify sentiment-objects from chinese sentences based on cascaded conditional random fields[J]. Journal of Chinese Information Processing. 2013, 27(3):69-76.

WU F, HUANG Y, YUAN Z. Domain-specific sentiment classification via fusing sentiment knowledge from multiple sources[J]. Information Fusion. 2017, 35:26-37.

郝志峰, 黄灿锦, 蔡瑞初, 等. 结合用户兴趣的微博信息传播模式挖掘[J]. 模式识别与人工智能. 2016, 29(10):924-935. HAO Z F, HUANG C J, CAI R C, et al. User interest related information diffusion pattern mining in microblog[J]. Pattern Recognition and Artificial Intelligence 2016, 29(10):924-935.

温雯, 吴彪, 蔡瑞初, 等. 基于多类别语义词簇的新闻读者情绪分类[J]. 计算机应用. 2016, 36(8):2076-2081. WEN W, WU B, CAI R C, et al. Emotion classification for news readers based on multi-category semantic word clusters[J]. Journal of Computer Applications. 2016, 36(8):2076-2081.

陈培文, 傅秀芬. 采用SVM方法的文本情感极性分类研究[J]. 广东工业大学学报. 2014(03):95-101. CHEN P W, FU X F. Research on sentiment classification of texts based on SVM[J]. Journal of Guangdong University of Technology. 2014(03):95-101.

LAFFERTY J D, MCCALLUM A, PEREIRA F C N. Conditional random fields:Probabilistic models for segmenting and labeling sequence data[C]//Proceedings of ICML.[S.l.:s.n.], 2001:282-289.

SUTTON C, MCCALLUM A, ROHANIMANESH K. Dynamic conditional random fields:Factorized probabilistic models for labeling and segmenting sequence data[J]. Journal of Machine Learning Research. 2007, 8:693-723.

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于混合高斯分布伪样本生成的情感分析方法

基于MABM的消费者情感倾向识别模型——以电影评论为例

短文本特征的组合加权方法

基于THUCTC的金融语料情感分析模型优化

双模态迭代交叉注意力融合集成框架