中文产品评论结构化引擎

doi:10.3969/j.issn.1006-2475.2014.07.001

计算机与现代化

• 人工智能 • 下一篇

中文产品评论结构化引擎

(1.中国电子科技集团公司第十五研究所航空信息系统部，北京 100083; 2.清华大学管理科学与工程系，北京 100084; 3.中国人民大学信息学院，北京 100872)

收稿日期:2014-05-26 出版日期:2014-07-16 发布日期:2014-07-17
作者简介:杨慧(1982-)，女，内蒙古满洲里人，中国电子科技集团公司第十五研究所航空信息系统部工程师，硕士，研究方向：数据挖掘，意见挖掘; 刘红岩(1968-)，女，山东人，清华大学管理科学与工程系教授，博士，研究方向：数据挖掘，意见挖掘; 何军(1962-)，男，北京人，中国人民大学信息学院副教授，博士，研究方向：数据库，数据挖掘，信息检索，计算机网络。
基金资助:
国家自然科学基金资助项目（70471006, 70621061, 60496325, 60573092）

A Structured System of Mining Chinese Product Reviews

(1. Department of Aviation Information System, The 15th Institute, China Electronics Technology Group Corporation, Beijing 100083, China;
2. Department of Management Science and Engineering, Tsinghua University, Beijing 100084, China;
3. School of Information, Renmin University of China, Beijing 100872, China)

Received:2014-05-26 Online:2014-07-16 Published:2014-07-17

摘要/Abstract

摘要： 为实现网络上非领域相关的中文产品评论信息的搜索及结构化，设计并实现中文产品评论结构化引擎，该引擎通过抓取网络上指定产品的评论，抽取评论中的特征与意见，判断意见的极型，将评论以（特征，意见，极型）的结构化形式输出给用户。其中特征意见抽取解决了产品隐形特征抽取问题。实验结果表明该引擎的特征意见召回率与准确率、意见极型判断准确率等指标可以满足用户需要。

关键词: 意见挖掘, 产品评论结构化, 特征抽取, 感情色彩判断

Abstract: We designed and implemented a structured system of mining Chinese product reviews. According to the user’s specified product names, the system could grab reviews about the product on Internet, extract product features and automatically determine the polarity of context-dependent opinions and then return a structured result of the reviews as the triple (feature, opinion, polarity). The experiment proved the effectiveness of the algorithms. And it made an acceptable precision and recall.

Key words: opinion mining, structured product review, feature extraction, sentiment analysis

中图分类号:

TP391

杨慧1，刘红岩2，何军3. 中文产品评论结构化引擎[J]. 计算机与现代化, doi: 10.3969/j.issn.1006-2475.2014.07.001.

YANG Hui1, LIU Hong-yan2, HE Jun3. A Structured System of Mining Chinese Product Reviews[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2014.07.001.

参考文献 19

[1]	Liu Bing, Hu Minqing, Cheng Junsheng. Opinion observer: Analyzing and comparing opinions on the Web[C]// Proceedings of the 14th International Conference on World Wide Web. 2005:342-351.
[2]	Hu Minqing, Liu Bing. Mining and summarizing customer reviews[C]// Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2004:168-177.
[3]	Turney P. Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews[C]// Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics. 2002:417-424.
[4]	Popescu A-M, Etzioni O. Extracting product features and opinions from reviews[C]// Proceedings of the 2005 Conference on Human Language Technology and Empirical Methods in Natural Language Processing. 2005:339-346.
[5]	Hu Minqing, Liu Bing. Mining opinion features in customer reviews[C]// Proceedings of the 19th AAAI National Conference on Artificial Intelligence. 2004:755-760.
[6]	Ghani R, Probst K, Liu Yan, et al. Fano: Text mining for product attribute extraction[J]. ACM SIGKDD Explorations Newsletter, 2006,8(1):41-48.
[7]	Ding Xiaowen, Liu Bing. The utility of linguistic rules in opinion mining[C]// Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 2007:811-812.
[8]	Gamon M, Aue A, Corston-Oliver S, et al. Pulse: Mining customer opinions from free text[C]// Proceedings of the 6th International Symposium on Intelligent Data Analysis. 2005:121-132.
[9]	Scaffidi C, Bierhoff K, Chang E, et al. Red opal: Product-feature scoring from reviews[C]// Proceedings of the 8th ACM Conference on Electronic Commerce. 2007:182-191.
[10]	郭冲,王振宇. 面向细粒度意见挖掘的情感本体树及自动构建[J]. 中文信息学报, 2013,27(5):75-83.
[11]	文涛,杨达,李娟. 中文软件评论挖掘系统的设计与实现[J]. 计算机工程与设计, 2013,34(1):163-167.
[12]	马晓玲,金碧漪,范并思. 中文文本情感倾向分析研究[J]. 情报资料工作, 2013,34(1):52-56.
[13]	陆文星,王燕飞. 中文文本情感分析研究综述[J]. 计算机应用研究, 2012,29(6):2014-2017.
[14]	李芳,何婷婷,宋乐. 评价主题挖掘及其倾向性识别[J]. 计算机科学, 2012,39(6):159-162.
[15]	Sebastiani F. Machine learning in automated text categorization[J]. ACM Computing Surveys, 2002,34(1):1-47.
[16]	Pang Bo, Lee L, Vaithyanathan S. Thumbs up? Sentiment classification using machine learning techniques[C]// Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing. 2002:79-86.
[17]	Yu Hong, Hatzivassiloglou V. Towards answering opinion questions: Separating facts from opinions and identifying the polarity of opinion sentences[C]// Proceedings of the 2003 Conference on Empirical Methods for Natural Language Processing. 2003:129-136.
[18]	董振东,董强. 《知网》情感分析用词语集[EB/OL]. http://www.keenage.com/cgi-bin/c_enroll.cgi, 2007-10-21.
[19]	Kaufman L, Rousseeuw P J. Finding Groups in Data: An Introduction to Cluster Analysis[M]. New York: Wiley, 1990.

中文产品评论结构化引擎

A Structured System of Mining Chinese Product Reviews

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献 19

相关文章 3

编辑推荐

Metrics

本文评价

[1]	王浩畅, 刘如意. 基于预训练模型的关系抽取研究综述[J]. 计算机与现代化, 2023, 0(01): 49-57.
[2]	佘维军，刘子平，杨卫芳. 基于改进LDA主题模型的产品特征抽取[J]. 计算机与现代化, 2016, 0(11): 1-6,57.
[3]	黄可望1，冯宗越2,3，朱嘉钢2,3. 基于NIB2DPCA的彩色图像过完整分块特征抽取方法[J]. 计算机与现代化, 2015, 0(12): 78-.