Extracting Emotion Tags from Comments of Microblog Commodities
Bocheng Li1,Yunqiu Zhang1(),Kaixi Yang2
1 College of Public Health, Jilin University, Changchun 130021, China 2 International School of Information Science & Engineering, Dalian University of Technology, Dalian 116620, China;
[Objective] This paper proposes a new method to collect emotion tags from microblog comments, aiming to improve the performance of feature-level data extraction. [Methods] First, we divided the evaluation units and extracted the explicit tags based on the dependency parsing and the extraction rules. Then, we revealed the implicit expression relationship in comments with the NodeRank algorithm. Finally, we retrieved the implicit tags to improve the accuracy of emotion tag retrieval. [Results] We examined the proposed method with the real online comments. The overall precision of the method was 83.6%, the recall rate was 87.1%, and the F value was 85.3%, which were better than the traditional methods. [Limitations] We did not fully utilize users’ general emotional expressions. [Conclusions] The proposed method based on dependency parsing and NodeRank algorithm can extract emotion tags effectively.
( (China Internet Information Center. The 43rd China Internet Development Statistics Report [R/OL].(2019-02-28). [2019-03-02]. http://cnnic.cn/gywm/xwzx/rdxw/20172017_7056/201902/t20190228_70643.htm. )
( Shi Wei, Wang Hongwei, He Shaoyi . Product Reviews Mining from Microblogging Based on Sentiment Analysis[J]. Journal of the China Society for Scientific and Technical Information, 2014,33(12):1311-1321.)
( Ding Shengchun, Wu Jingchanyuan, Li Hongmei . Chinese Micro-blogging Opinion Recognition Based on SVM Model[J]. Journal of the China Society for Scientific and Technical Information, 2016,35(12):1235-1243.)
[6]
胡默之 . 中文微博观点句识别及评价对象抽取[D]. 上海:上海交通大学, 2015.
[6]
( Hu Mozhi . Recognition of Chinese Microblog Sentiment Polarity and Extraction of Opinion Target[D]. Shanghai: Shanghai Jiaotong University, 2015.)
[7]
鲍佳娜 . 基于汽车领域的中文微博意见挖掘研究[D]. 上海:上海交通大学, 2014.
[7]
( Bao Jia’na . A Study on Chinese Microblog Opinion Mining Based on Automobile Domain[D]. Shanghai: Shanghai Jiaotong University, 2014.)
( Ding Shengchun, Meng Meiren, Li Xiao . Study of Subjective Sentence Identification Oriented to Chinese Microblog[J]. Journal of the China Society for Scientific and Technical Information, 2014,33(2):175-182.
( Qiu Yunfei, Ni Xuefeng, Shao Liangshan . Research on Extracting Method of Commodities Implicit Opinion Tar-gets[J]. Computer Engineering and Applications, 2015,51(19):114-118.)
[11]
Nikfarjam A, Emadzadeh E, Gonzalez G . A Hybrid System for Emotion Extraction from Suicide Notes[J]. Biomedical Informatics Insights, 2012,5(S1):165-174.
[12]
Feng C, Liao C, Liu Z, et al. A Hybrid Method of Sentiment Key Sentence Identification Using Lexical Semantics and Syntactic Dependencies [C]// Proceedings of the 2014 Asia-Pacific Web Conference. Springer, 2014: 11-22.
( Dong Lili, Zhao Fanrong, Zhang Xiang . Analysing Propensity of Product Reviews Based on Domain Ontology and Sentiment Lexicon[J]. Computer Applications and Software, 2014,31(12):104-108.)
[14]
Wang F, Xu Y, Wu Y , et al. Predicting the Semantic Orientation of Emoticons[J]. Journal of Computational Information Systems, 2013,9(6):2391-2398.
( Lan Tian, Guo Gongde . Bursty Topic Detection Based on Word Co-Occurrence and Emotions[J]. Computer Systems and Applications, 2016,25(8):101-108.)
[16]
Popescu A M, Etzioni O . Extracting Product Features and Opinions from Reviews[A]// Kao A, Poteet S R. Natural Language Processing and Text Mining[M]. 2007: 9-28.
[17]
Kumar V R, Raghuveer K . Dependency Driven Semantic Approach to Product Features Extraction and Summarization Using Customer Reviews[J]. Advances in Intelligent Systems & Computing, 2013,178:225-238.
( Xia Mengnan, Du Yongping, Zuo Benxin . Micro-blog Opinion Analysis Based on Syntactic Dependency and Feature Combination[J]. Journal of Shandong University: Science Edition, 2014,49(11):22-30.)
( Nie Hui, Du Jiazhong . Using Dependency Parsing Pattern to Extract Product Feature Tags[J]. New Technology of Library and Information Service, 2014(12):44-50.)
( Wu Qinglin, Zhou Tianhong . Analysis of the Public Opinion of Chinese Microblog Based on Topic Clustering and Emotional Intensity[J]. Information Studies: Theory & Practice, 2016,39(1):109-112.)
( Li Guangmin, Chen Chi, Xing Jiang , et al. Overview of Extracting Product Feature from Text Reviews[J]. Journal of Modern Information, 2016,36(8):168-173.)
( Tang Xiaobo, Lan Yuting . Sentiment Analysis of Microblog Product Reviews Based on Feature Ontology[J]. Library and Information Service, 2016,60(16):121-127.)
[23]
Tuarob S, Tucker C S. A Product Feature Inference Model for Mining Implicit Customer Preferences Within Large Scale Social Media Networks [C]// Proceedings of the ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. 2015.
( He Yanxiang, Liu Jianbo, Sun Songtao , et al. Product Reviews Sentiment Classification in Micro-blog Based on Cascaded Conditional Random Field[J]. Journal of Shandong University: Science Edition, 2015,50(11):67-73.)
[26]
Li P, Qiu X. NodeRank: An Algorithm to Assess State Enumeration Attack Graphs [C]// Proceedings of the 8th International Conference on Wireless Communications, Networking and Mobile Computing. IEEE, 2012.
