图书情报工作

图书情报工作 >

2014 , Vol. 58 >Issue 14: 12 - 20

DOI: https://doi.org/10.13266/j.issn.0252-3116.2014.14.002

专题：大数据环境下的情报方法研究与应用

一种基于句法分析的情感标签抽取方法

  • 李纲 ,
  • 刘广兴 ,
  • 毛进 ,
  • 叶光辉
展开
  • 1. 武汉大学信息资源研究中心;
    2. 武汉大学信息管理学院
李纲，武汉大学信息资源研究中心副主任，教授，博士生导师；刘广兴，武汉大学信息管理学院硕士研究生，通讯作者，E-mail：lgx1431@126.com；毛进，武汉大学信息资源研究中心博士研究生；叶光辉，武汉大学信息管理学院博士研究生。

收稿日期: 2014-04-10

  修回日期: 2014-05-12

  网络出版日期: 2014-07-20

基金资助

本文系国家自然科学基金项目“科研团队动态演化规律研究”（项目编号：71273196）、北京市财政项目“大数据环境下情报服务规范化体系建设”（项目编号：PXM2013_178214_000010）和武汉大学自主科研项目（人文社会科学）“网络视角下的应急情报体系建设主题研究”（项目编号：274014，得到“中央高校基本科研业务费专项资金”资助）的研究成果之一。

收起

A Sentiment Label Extraction Method Based on Dependency Parsing

  • Li Gang ,
  • Liu Guangxing ,
  • Mao Jin ,
  • Ye Guanghui
Expand
  • 1. Center for the Studies of Information Resource, Wuhan University, Wuhan 430072;
    2. Schol of Information Management, Wuhan University, Wuhan 430072

Received date: 2014-04-10

  Revised date: 2014-05-12

  Online published: 2014-07-20

Fold

摘要

指出情感标签由评价对象和情感词组成，包含评论的关键要素，能清楚地表达评价者的观点意见。提出一种针对产品网络评论的情感标签抽取模型，利用依存句法分析设计情感标签抽取算法，通过情感极性计算对抽取出的情感标签进行过滤。通过放宽的抽取规则与情感极性过滤相结合，以提高情感标签的召回率，实现潜在评价对象的抽取。最后用网络抓取的产品评论语料作为测试数据集对模型进行测试，获得较高的抽取准确率和召回率，并对模型中存在的问题进行总结，作为模型改善的指导。

关键词： 情感标签; 观点挖掘; 依存句法分析; 产品评论

本文引用格式

李纲 , 刘广兴 , 毛进 , 叶光辉 . 一种基于句法分析的情感标签抽取方法[J]. 图书情报工作, 2014 , 58(14) : 12 -20 . DOI: 10.13266/j.issn.0252-3116.2014.14.002

Abstract

As a collection of evaluation object and sentiment words, sentiment label contains key elements of on user reviews and can effectively reflect their core contents. This paper proposes a model of extracting sentiment label from products reviews in Web. Based on dependency parsing technology, it designs the algorithm of sentiment label extraction, and filters extracted sentiment labels by setting the threshold for emotional polarity. Combined with the relaxed rules of extraction and emotional polarity filter, it gets higher recall and extracts the potential target of reviews. Finally, it captures online reviews of product as the test data set to test the model and receive a higher precision and recall. To improve the model, the problems in the model are also summarized.

Key words： sentiment label; opinion mining; dependency parsing; product review

参考文献

[1] 中国互联网信息中心(CNNIC).第33次《中国互联网络发展状况统计报告》[EB/OL].[2014-02-01].http://www.cnnic.cn/hlwfzyj/hlwxzbg/hlwtjbg/201403/P020140305346585959798.pdf

[2] Popescu A M, Etzioni O. Extracting product features and opinions from reviews[M]//Kao A,Poteet S R.Natural Language Processing and Text Mining. London:Springer, 2007:9-28.

[3] Hatzivassiloglou V, McKeown K R. Predicting the semantic orientation of adjectives[C]//Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics. Stroudsburg :ACL, 1997:174-181.

[4] Li Zhuang, Feng Jing, Zhu Xiaoyan. Movie review mining and summarization[C]//Proceedings of the 15th ACM International Conference on Information and Knowledge Management. New York:ACM, 2006:43-50.

[5] Pak A, Paroubek P. Twitter as a corpus for sentiment analysis and opinion mining[C]//Proceedings of the Seventh Conference on International Language Resources and Evaluation (LREC'10).Valletta:ELRA, 2010:1320-1326.

[6] Hu Minqing, Liu Bing. Mining and summarizing customer reviews[C]//Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York :ACM, 2004:168-177.

[7] 朱嫣岚,闵锦,周雅倩,等.基于HowNet的词汇语义倾向计算[J].中文信息学报, 2006, 20(1):14-20.

[8] Kim H D, Zhai Chengxiang. Generating comparative summaries of contradictory opinions in text[C]//Proceedings of the 18th ACM Conference on Information and Knowledge Management. New York:ACM, 2009:385-394.

[9] Turney P D. Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews [C]//Proceedings of the 40th Annual Meeting on Association for Computational Linguistics. Stroudsburg :ACL, 2002:417-424.

[10] Sun Wenjun,Pan Mingyang ,Ye Qiang. Comparative study on objective and subjective emotional tendencies of online reviews from different sources[C]//Internet Technology and Applications (iTAP), 2011 International Conference on. Wuhan:IEEE, 2011:1-4.

[11] Mullen T, Collier N. Sentiment analysis using support vector machines with diverse information sources[C]//Barcelona :EMNLP, 2004:412-418.

[12] Pang B, Lee L. Opinion mining and sentiment analysis[J]. Foundations and Trends in Information Retrieval, 2008, 2(1/2):1-135.

[13] 姚天昉,程希文,徐飞玉,等. 文本意见挖掘综述[J]. 中文信息学报, 2008, 22(3):71-80.

[14] 张紫琼, 叶强, 李一军. 互联网商品评论情感分析研究综述[J]. 管理科学学报, 2010, 13(6):84-96.

[15] Kim S M, Hovy E. Determining the sentiment of opinions[C]//Proceedings of the 20th International Conference on Computational Linguistics. Stroudsburg :ACL, 2004:1367-1373.

[16] LTP[EB/OL]. [2013-10-24].http://www.ltp-cloud.com/download/.

[17] Yi J, Nasukawa T, Bunescu R, et al. Sentiment analyzer:Extracting sentiments about a given topic using natural language processing techniques[C]//Proceedings of 2003 IEEE International Conference on Data Mining. Melbourne:IEEE, 2003:427-434.

[18] HowNet[EB/OL] . [2013-10-24].http://www.keenage.com/html/c_index.html.

[19] 刘鸿宇. 情感标签抽取相关技术研究[D]. 哈尔滨:哈尔滨工业大学, 2010.

[20] 刘群, 李素建. 基于《知网》 的词汇语义相似度计算[J]. 中文计算语言学, 2002, 7(2):59-76.

Options
文章导航

/

〈 〉