微博短文本的情绪分析方法

doi:10.6040/j.issn.1671-9352.5.2016.034

山东大学学报（理学版） ›› 2017, Vol. 52 ›› Issue (7): 80-90.doi: 10.6040/j.issn.1671-9352.5.2016.034

微博短文本的情绪分析方法

施寒潇,厉小军,郝腾达,柳虹,朱柳青

浙江工商大学计算机与信息工程学院, 浙江杭州 310018

收稿日期:2016-10-11 出版日期:2017-07-20 发布日期:2017-07-07
作者简介:施寒潇(1977— ),男,博士,副教授,研究方向为社会网络、情绪分析. E-mail:hxshi@mail.zjgsu.edu.cn
基金资助:
国家社会科学基金资助项目(14BTQ047)

Emotion analysis on Microblog short text

SHI Han-xiao, LI Xiao-jun, HAO Teng-da, LIU Hong, ZHU Liu-qing

School of Computer Science &
Information Engineering, Zhejiang Gongshang University, Hangzhou 310018, Zhejiang, China

Received:2016-10-11 Online:2017-07-20 Published:2017-07-07

摘要/Abstract

摘要： 面向微博短文本的情绪分析研究是当前的研究热点。提出了利用依存句法对微博短文本进行分析,抽取关系对,并设计相应的方法用于情感计算,其结果作为特征加入到情绪句判别模型之中;同时设计出情绪句判别规则,在分类模型之前或者之后利用规则进行预处理或者后处理,提高情绪句的判别正确率;最后使用NLP&2013中文微博数据,通过实验证明研究方法的有效性,在性能指标上相比评测最好成绩有了进一步提高。

关键词: 情绪分析, 依存句法, 情绪短文本, 特征抽取

Abstract: Studies on emotion analysis oriented to Microblog short text are a hot issue in present research. In this paper, dependency grammar was used to analyse Microblog short text and extract relation pair. We proposed the corresponding methods to compute sentiment value and add the corresponding results to discriminant model of sentiment sentences as features. We also proposed discrimination rules of sentiment sentences and utilized the rules to correspondingly preprocess or postprocess before or after the classification model in order to improve the discrimination rate of sentiment sentences. Finally, we used NLP&CC’2013 Chinese Microblog data to testify the effectiveness of our method through experiments. Results show our works have better performance comparing to the best evaluation in that year.

Key words: emotion analysis, feature extraction, dependency syntax, emotion short text

中图分类号:

TP391

施寒潇,厉小军,郝腾达,柳虹,朱柳青. 微博短文本的情绪分析方法[J]. 山东大学学报（理学版）, 2017, 52(7): 80-90.

SHI Han-xiao, LI Xiao-jun, HAO Teng-da, LIU Hong, ZHU Liu-qing. Emotion analysis on Microblog short text[J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2017, 52(7): 80-90.

参考文献

[1] PANG B, LEE L. Opinion mining and sentiment analysis: foundations and trends[J]. Information Retrieval, 2008, 2(1-2):1-135.
[2] CUI H, MITTAL V, DATAR M. Comparative experiments on sentiment classification for online product reviews[C] // National Conference on Artificial Intelligence and the Eighteenth Innovative Applications of Artificial Intelligence Conference. New York: ACM, 2006: 1265-1270.
[3] LI Shoushan, HUANG Churen, ZHOU Guodong, et al. Employing personal/impersonal views in supervised and semi-supervised sentiment classification[C] // Proceedings of Annual Meeting of the Association for Computational Linguistics. Somerset: Association for Computational Linguistics, 2010: 414-423.
[4] BACCIANELLA S, ESULI A, SEBASTIANI F. Multi-facet rating of product reviews[C] // Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval(ECIR'09). Berlin: Springer, 2009:461-472.
[5] GYAMFI Y, WIEBE J, MIHALCEA R, et al. Integrating knowledge for subjectivity sense labeling[C] // Proceedings of Human Language Technologies: the 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics. New York: ACM, 2009: 10-18.
[6] QUAN Changqin, REN Fuji. Construction of a blog emotion corpus for Chinese emotional expression analysis[C] // Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing. New York: Association for Computational Linguistics, 2009: 1446-1454.
[7] 姚源林, 王树伟, 徐睿峰,等. 面向微博文本的情绪标注语料库构建[J]. 中文信息学报, 2014, 28(5):83-91. YAO Yuanlin, WANG Shuwei, XU Ruifeng, et al. The construction of an emotion annotated corpus on micro-blog text[J]. Journal of Chinese Information Processing, 2014, 28(5):83-91.
[8] PANG B, LEE L, VAITHYANATHAN S. Thumbs up?: sentiment classification using machine learning techniques[C] // Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing. New York: Association for Computational Linguistics, 2002: 79-86.
[9] JIN Wei, HO H H, SRIHARI R K. OpinionMiner: a novel machine learning system for web opinion mining and extraction[C] // ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2009: 1195-1204.
[10] XIA Rui, WANG Cheng, DAI Xinyu, et al. Co-training for semi-supervised sentiment classification based on dual-view bags-of-words representation[J]. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing. New York: Association for Computational Linguistics, 2015: 1054-1063.
[11] GO A, BHAYANI R, HUANG Lei. Twitter sentiment classification using distant supervision[R/OL]. Stanford, 2009. [2016-02-15]. http://www-cs.stanford.edu/people/alecmgo/papers/TwitterDistantSupervision09.pdf.
[12] BARBOSA L, FENG Junlan. Robust sentiment detection on twitter from biased and noisy data[C] // International Conference on Computational Linguistics. New York: ACM, 2010: 36-44.
[13] WU Fangzhou, HUANG Yongfeng, SONG Yangqiu, et al. Towards building a high-quality microblog-specific Chinese sentiment lexicon[J]. Decision Support Systems, 2016, 87:39-49.
[14] LI Weiyuan, XU Hua. Text-based emotion classification using emotion cause extraction[J]. Expert Systems with Applications, 2013, 41(3):1742-1749.
[15] DO H J, CHOI H J. Korean twitter emotion classification using automatically built emotion lexicons and fine-grained features[J]. Shanghai Management Science, 2015:142-150.
[16] SEO K J, NAM K C, CHOI K S. A probabilistic model of the dependency parse of the variable-word-order languages by using ascending dependency[J]. Computer Processing of Oriental Languages, 1999, 12(3):309-322.
[17] 冯时, 付永陈, 阳峰,等. 基于依存句法的博文情感倾向性分析研究[J]. 计算机研究与发展, 2012, 49(11):2395-2406. FENG Shi, FU Yongchen, YANG Feng, et al. Blog sentiment orientation analysis based on dependency parsing[J]. Journal of Compute Research and Development, 2012, 49(11):2395-2406.
[18] 郝雷红. 现代汉语否定副词研究[D]. 北京: 首都师范大学, 2003. HAO Leihong. A study on negative adverbs in modern Chinese[D]. Beijing: Capital Normal University, 2003.
[19] 蔺璜, 郭姝慧. 程度副词的特点范围与分类[J]. 山西大学学报, 2003, 26(2):71-74. LIN Huang, GUO Shuhui. On the characteristics, range and classification of adverbs of degree[J]. Journal of Shanxi University, 2003, 26(2):71-74.
[20] SHI Hanxiao, CHEN Wei, LI Xiaojun. Opinion sentence extraction and sentiment analysis for chinese microblogs[C] // Natural Language Processing and Chinese Computing. Berlin: Springer Verlag, 2013: 417-423.

多维度评价

Viewed

Full text

Abstract

Cited

Shared

Discussed

微博短文本的情绪分析方法

Emotion analysis on Microblog short text

PDF (PC)

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 2

多维度评价

本文评价

推荐阅读 0

[1]	杜漫,徐学可,杜慧,伍大勇,刘悦,程学旗. 面向情绪分类的情绪词向量学习[J]. 山东大学学报（理学版）, 2017, 52(7): 52-58.
[2]	杨佳能, 阳爱民, 周咏梅. 基于语义分析的中文微博情感分类方法[J]. 山东大学学报（理学版）, 2014, 49(11): 14-21.