计算机科学 ›› 2020, Vol. 47 ›› Issue (6): 194-200.doi: 10.11896/jsjkx.200200127

• 人工智能 • 上一篇    下一篇

面向评论的方面级情感分析综述

张严, 李天瑞   

  1. 西南交通大学信息科学与技术学院 成都611756
  • 收稿日期:2020-02-28 出版日期:2020-06-15 发布日期:2020-06-10
  • 通讯作者: 李天瑞(trli@swjtu.edu.cn)
  • 作者简介:illidanying@163.com
  • 基金资助:
    国家重点研发计划(2017YFB1401400)

Review of Comment-oriented Aspect-based Sentiment Analysis

ZHANG Yan, LI Tian-rui   

  1. School of Information Science and Technology,Southwest Jiaotong University,Chengdu 611756,China
  • Received:2020-02-28 Online:2020-06-15 Published:2020-06-10
  • About author:ZHANG Yan,born in 1996,postgra-duate.His main research interests include sentiment analysis and natural language processing.
    LI Tian-rui,born in 1969,Ph.D,professor,Ph.D supervisor,is a member of China Computer Federation.His main research interests include big data intelligence,rough sets,and granular computing.
  • Supported by:
    This work was supported by the National Key R&D Program of China (2017YFB1401400)

摘要: 面向评论的方面级情感分析(Aspect-Based Sentiment Analysis,ABSA)是文本分析的关键问题之一。随着社交媒体的迅猛发展,网络评论的数量呈爆炸式增长,越来越多的人愿意在网络上表达自己的态度和情感,但是网络评论的风格与质量参差不齐,如何从中准确地提取用户的方面观点倾向成为了一个难点。同时,用户在浏览评论时也更加关注一些细粒度的信息,对评论进行方面级情感分析能够帮助用户更好地做出决策。文中首先介绍了方面级情感分析的相关概念与问题描述;然后从方面提取和基于方面的情感分析两个角度介绍了近年来国内外方面级情感分析的研究现状;随后分享了方面级情感分析任务相关的语料库和情感词典资源;最后分析了方面级情感分析所面临的挑战,以及未来可能的研究方向。

关键词: 方面提取, 观点倾向, 情感分析, 网络评论

Abstract: Comment-oriented aspect-level sentiment analysis is one of the key issues in text analysis.With the rapid development of social media,the number of online comments has exploded.More and more people are willing to express their attitudes and emotions on the Internet,but the style and quality of online comments are uneven.How to extract the user’s perspective accurately has become a difficulty.At the same time,users also pay more attention to some fine-grained information when browsing comments,and performing aspect-level sentimentanalysis on comments can help users make decisions better.This paper first introduces the related concepts and problem descriptions of aspect-level sentimentanalysis,and then introduces the research status of aspect-level sentiment analysis at home and abroad in recent years from aspects of aspect extraction and aspect-based sentiment analysis.The corpus and sentiment dictionary resources related to the aspect-level sentiment analysis task are shared,and finally the challenges faced by the aspect-level sentiment analysis and the possible future research directions are analyzed.

Key words: Aspect extraction, Internet reviews, Point of view, Sentiment analysis

中图分类号: 

  • TP391
[1]LI Y,LI Z X,TENG L,et al.Comment Sentiment Analysis and Sentiment Words Detection Based on Attention Mechanism[J].Computer Science,2020,47(1):186-192.
[2]LIU C,RAN Q.Attitudinal Resources in Customers’ Remarks in CSC and Their Impacts on Potential Customers’ Purchase Decisions[J].Journal of University of Science and Technology Beijing (Social Sciences Edition),2017(6):2.
[3]PANG B,LEE L.Opinion mining and sentiment analysis[J].Foundations and Trends? in Information Retrieval,2008,2(1/2):1-135.
[4]LIU B.Sentiment analysis and opinion mining[J].Synthesis Lectures on Human Language Technologies,2012,5(1):1-167.
[5]BAHDANAU D,CHO K,BENGIO Y.Neural machine translation by jointly learning to align and translate[J].arXiv:1409.0473,2014.
[6]DONG L,WEI F,TAN C,et al.Adaptive recursive neural network for target-dependent twitter sentiment classification[C]//Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics.2014:49-54.
[7]HOCHREITER S,SCHMIDHUBER J.Long short-term memory[J].Neural Computation,1997,9(8):1735-1780.
[8]WANG Y,HUANG M,ZHAO L.Attention-based LSTM for aspect-level sentiment classification[C]//Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing.2016:606-615.
[9]TANG D,QIN B,FENG X,et al.Effective LSTMs for target-dependent sentiment classification[J].arXiv:1512.01100,2015.
[10]ZHANG M,ZHANG Y,VO D T.Gated neural networks for targeted sentiment analysis[C]//Thirtieth AAAI Conference on Artificial Intelligence.2016.
[11]CHEN P,SUN Z,BING L,et al.Recurrent attention network on memory for aspect sentiment analysis[C]//Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing.2017:452-461.
[12]HAN Z M,LI M Q,LIU W,et al.Survey of studies on aspect-based opinion mining of Internet[J].Journal of Software,2018,29(2):417-441.
[13]KIM S M,HOVY E.Determining the sentient of opinions[C]//Proceedings of the 20th International Conference on Computational Linguistics.Association for Computational Linguistics,2004:1367.
[14]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.Association for Computational Linguistics,2002:79-86.
[15]BARBOSA L,FENG J.Robust sentiment detection on twitter from biased and noisy data[C]//Proceedings of the 23rd International Conference on Computational Linguistics:posters.Association for Computational Linguistics,2010:36-44.
[16]CRUZ F L,TROYANO J A,ENRIQUEZ F,et al.‘Long autonomy or long delay?’The importance of domain in opinion mining[J].Expert Systems with Applications,2013,40(8):3174-3184.
[17]JIANG P,ZHANG C,FU H,et al.An approach based on tree kernels for opinion mining of online product reviews[C]//2010 IEEE International Conference on Data Mining.IEEE,2010:256-265.
[18]LIU K,XU L,ZHAO J.Extracting opinion targets and opinion words from online reviews with graph co-ranking[C]//Procee-dings of the 52nd Annual Meeting of the Association for Computational Linguistics.2014:314-324.
[19]JIN W,HO H H,SRIHARI R K.A novel lexicalized HMM-based learning framework for web opinion mining[C]//Procee-dings of the 26th Annual International Conference on Machine Learning.Citeseer,2009:465-472.
[20]JAKOB N,GUREVYCH I.Extracting opinion targets in a single-and cross-domain setting with conditional random fields[C]//Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing.Association for Computational Linguistics,2010:1035-1045.
[21]CHEN B F,HAO Z F,CAI R C,et al.Research on fine-grained sentiment analysis method for automobile reviews [J].Journal of Guangdong University of Technology,2017,34(3):8-14.
[22]LI F,HAN C,HUANG M,et al.Structure-aware review mining and summarization[C]//Proceedings of the 23rd International Conference on Computational Linguistics.Association for Computational Linguistics,2010:653-661.
[23]HOFMANN T.Probabilistic latent semantic indexing[C]//Proceedings of the 22nd Annual International ACM SIGIRConfe-rence on Research and Development in Information Retrieval.1999:50-57.
[24]BLEI D M,NG A Y,JORDAN M I.Latent dirichlet allocation[J].Journal of Machine Learning research,2003,3(Jan):993-1022.
[25]HU M,LIU B.Mining and summarizing customer reviews[C]//Proceedings of the tenth ACM SIGKDD International Confe-rence on Knowledge Discovery and Data Mining.ACM,2004:168-177.
[26]LI S,YE Q,LI Y J,et al.Research on Product Feature Mining Methods of Chinese Online Customer Reviews [J].Journal of Management Sciences,2009(2):146-156.
[27]XIAO L,CHEN G,LIU J Y.Research on Recognition of Enterprise Product Level Competitors Based on Sentiment Analysis-Using User Reviews as Data Source [J].Library and Information Service,2016,60(1):83-90,97.
[28]SAMHA A K,LI Y,ZHANG J.Aspect-based opinion extraction from customer reviews[J].arXiv:1404.1982,2014.
[29]ZHU J,WANG H,ZHU M,et al.Aspect-based opinion polling from customer reviews[J].IEEE Transactions on Affective Computing,2011,2(1):37-49.
[30]LU Y,ZHAI C.Opinion integration through semi-supervised topic modeling[C]//Proceedings of the 17th International Conference on World Wide Web.ACM,2008:121-130.
[31]ANDRZEJEWSKI D,ZHU X,CRAVEN M.Incorporating domain knowledge into topic modeling via Dirichlet forest priors[C]//Proceedings of the 26th Annual International Conference on Machine Learning.2009:25-32.
[32]WANG B,WANG H.Bootstrapping both product features and opinion words from Chinese customer reviews with cross-inducing[C]//Proceedings of the Third International Joint Conference on Natural Language Processing.2008.
[33]PENG Y,WAN C X,JIANG T J,et al.Product Feature and Emotion Word Extraction Based on Semantic Constraint LDA [J].Journal of Software,2017,28(3):676-693.
[34]YANG X,SU J.Coreference resolution using semantic relatedness information from automatically discovered patterns[C]//Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics.2007:528-535.
[35]LANG J,XIN Z,QIN B,et al.Coreference resolution with integrated multiple background semantic knowledge[J].Journal of Chinese Information Processing,2009,23(3):3-9.
[36]HAI Z,CHANG K,CONG G,et al.An association-based unified framework for mining features and opinion words[J].ACM Transactions on Intelligent Systems and Technology (TIST),2015,6(2):26.
[37]AMPLAYO R K,LEE S,SONG M.Incorporating product description to sentiment topic models for improved aspect-based sentiment analysis[J].Information Sciences,2018,454:200-215.
[38]ZENG L,LI F.A classification-based approach for implicit feature identification[M]//Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data.Berlin:Springer,2013:190-202.
[39]SUN L,LI S,LI J Y,et al.A novel context-based implicit feature extracting method[C]//2014 International Conference on Data Science and Advanced Analytics (DSAA).IEEE,2014:420-424.
[40]XU H,ZHANG F,WANG W.Implicit feature identification in Chinese reviews using explicit topic mining model[J].Know-ledge-Based Systems,2015,76:166-175.
[41]TANG X B,LAN Y T.Sentiment Analysis of Weibo Product Reviews Based on Feature Ontology [J].Library and Information Service,2016,60(16):121-127,136.
[42]DING X,LIU B,YU P S.A holistic lexicon-based approach to opinion mining[C]//Proceedings of the 2008 International Conference on Web Search and Data Mining.2008:231-240.
[43]NGUYEN T H,SHIRAI K.Aspect-based sentiment analysisusing tree kernel based relation extraction[C]//International Conference on Intelligent Text Processing and Computational Linguistics.Cham:Springer,2015:114-125.
[44]LIPENKOVA J.A system for fine-grained aspect-based sentiment analysis of Chinese[C]//Proceedings of ACL-IJCNLP 2015 System Demonstrations.2015:55-60.
[45]VO D T,ZHANG Y.Target-dependent twitter sentiment classification with rich automatic features[C]//Twenty-Fourth International Joint Conference on Artificial Intelligence.2015.
[46]KIRITCHENKO S,ZHU X,CHERRY C,et al.NRC-Canada-2014:Detecting aspects and sentiment in customer reviews[C]//Proceedings of the 8th Internationalworkshop on Semantic Evaluation (SemEval 2014).2014:437-442.
[47]BAO H,LI S T.Object-level sentiment analysis combining Bi-LSTM and positional relationship [J].Information System Engineering,2018(3):149-151.
[48]XUE W,LI T.Aspect based sentiment analysis with gated convolutional networks[J].arXiv:1805.07043,2018.
[49]TANG D,QIN B,LIU T.Aspect level sentiment classification with deep memory network[J].arXiv:1605.08900,2016.
[50]WANG J,SUN C,LI S,et al.Aspect Sentiment Classification Towards Question-Answering with Reinforced Bidirectional Attention Network[C]//Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics.2019:3548-3557.
[51]WANG W,PAN S J,DAHLMEIER D,et al.Recursive neural conditional random fields for aspect-based sentiment analysis[J].arXiv:1603.06679,2016.
[52]YIN Y,SONG Y,ZHANG M.Document-level multi-aspect sentiment classification as machine comprehension[C]//Procee-dings of the 2017 Conference on Empirical Methods in Natural Language Processing.2017:2044-2054.
[53]LI X,BING L,LI P,et al.A unified model for opinion target extraction and target sentiment prediction[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2019:6714-6721.
[54]LUO H,LI T,LIU B,et al.DOER:Dual Cross-Shared RNN for Aspect Term-Polarity Co-Extraction[J].arXiv:1906.01794,2019.
[55]WIEBE J,WILSON T,CARDIE C.Annotating expressions of opinions and emotions in language[J].Language Resources and Evaluation,2005,39(2/3):165-210.
[56]MUKHERJEE A.Extracting aspect specific sentiment expressions implying negative opinions[C]//International Conference on Intelligent Text Processing and Computational Linguistics.Cham:Springer,2016:194-210.
[57]PONTIKI M,GALANIS D,PAPAGEORGIOU H,et al.Semeval-2015 task 12:Aspect based sentiment analysis[C]//Proceedings of the 9th International workshop on Semantic Evaluation (SemEval 2015).2015:486-495.
[58]STONE P J,DUNPHY D C,SMITH M S.The General Inquirer: A Computer Approach to Content Analysis[M].Oxford:The M.I.T.Press,1968:375-376.
[59]BACCIANELLA S,ESULI A,SEBASTIANI F.SentiWordNet 3.0:an enhanced lexical resource for sentiment analysis and opinion mining[C]//Proceedings of the International Conference on Language Resources and Evaluation.2010:2200-2204.
[60]KU L W,CHEN H H.Mining opinions from the Web:Beyond relevance retrieval[J].Journal of the American Society for Information Science and Technology,2007,58(12):1838-1850.
[61]CHEN W T,LIN S C,HUANG S L,et al.E-HowNet and automatic construction of a lexical ontology[C]//Proceedings of the 23rd International Conference on Computational Linguistics:Demonstrations.Association for Computational Linguistics,2010:45-48.
[62]CHEN J M.Construction and application of Chinese emotional vocabulary ontology [D].Dalian:Dalian University of Technology,2009.
[63]PENNEBAKER J W,BOYD R L,JORDAN K,et al.The development and psychometric properties of LIWC 200[C]//LIWC 2007 Manual,2015,29(11):1020-1025.
[64]KAUR A,GUPTA V.A survey on sentiment analysis and opinion mining techniques[J].Journal of Emerging Technologies in Web Intelligence,2013,5(4):367-371.
[65]SHEN C L,ZHANG L,WU L Q,et al.Sentiment Classification Towards Question-Answering Based on Bidirectional Attention Mechanism[J].Computer Science,2019,46(7):151-156.
[66]LIU Q Y,ZHANG D,WU L Q,et al.Multi-modal Sentiment Analysis with Context-augmented LSTM[J].Computer Science,2019,46(11):181-185.
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