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Artificial Intelligence Review

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01.12.2016

Aspect extraction in sentiment analysis: comparative analysis and survey

verfasst von: Toqir A. Rana, Yu-N Cheah

Erschienen in: Artificial Intelligence Review | Ausgabe 4/2016

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Abstract

Sentiment analysis (SA) has become one of the most active and progressively popular areas in information retrieval and text mining due to the expansion of the World Wide Web (WWW). SA deals with the computational treatment or the classification of user’s sentiments, opinions and emotions hidden within the text. Aspect extraction is the most vital and extensively explored phase of SA to carry out the classification of sentiments in precise manners. During the last decade, enormous number of research has focused on identifying and extracting aspects. Therefore, in this survey, a comprehensive overview has been attempted for different aspect extraction techniques and approaches. These techniques have been categorized in accordance with the adopted approach. Despite being a traditional survey, a comprehensive comparative analysis is conducted among different approaches of aspect extraction, which not only elaborates the performance of any technique but also guides the reader to compare the accuracy with other state-of-the-art and most recent approaches.

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Agraval R, Srikant R (1994) Fast algorithms for mining association rules in large data bases. In: 20th inter-national conference on very large databases. Santiagom

Bafna K, Toshniwal D (2013) Feature based summarization of customers’ reviews of online products. Proc Comput Sci 22:142–151CrossRef

Bagheri A, Saraee M, De Jong F (2013a) Care more about customers: unsupervised domain-independent aspect detection for sentiment analysis of customer reviews. Knowl Based Syst 52:201–213CrossRef

Bagheri A, Saraee M, de Jong F (2013b) An unsupervised aspect detection model for sentiment analysis of reviews. In: Natural language processing and information systems. Springer, pp 140–151

Bancken W, Alfarone D, Davis J (2014) Automatically detecting and rating product aspects from textual customer reviews. In: (To appear in) Proceedings of DMNLP workshop at ECML/PKDD

Blei DM, Ng AY, Jordan MI (2003) Latent dirichlet allocation. J Mach Learn Res 3:993–1022MATH

Cambria E, Schuller B, Xia Y, Havasi C (2013) New avenues in opinion mining and sentiment analysis. IEEE Intell Syst 28(2):15–21CrossRef

Cambria E, Speer R, Havasi C, Hussain A (2010) Senticnet: a publicly available semantic resource for opinion mining. In: AAAI fall symposium: commonsense knowledge, Vol. 10, p 02

Chen L, Qi L, Wang F (2012) Comparison of feature-level learning methods for mining online consumer reviews. Expert Syst Appl 39(10):9588–9601CrossRef

Choi Y, Cardie C (2010) Hierarchical sequential learning for extracting opinions and their attributes. In: Proceedings of the ACL 2010 conference short papers. Association for computational linguistics, pp 269–274

Cruz FL, Troyano JA, Enrquez F, Ortega FJ, Vallejo CG (2013) Long autonomy or long delay?’The importance of domain in opinion mining. Expert Syst Appl 40(8):3174–3184CrossRef

Ding X, Liu B, Yu PS (2008) A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 international conference on web search and data mining. ACM, pp 231–240. http://dl.acm.org/citation.cfm?id=1341561

Du J, Chan W, Zhou X (2014) A product aspects identification method by using translation-based language model. In: 2014 22nd international conference on IEEE pattern recognition (ICPR), pp 2790–2795

Eirinaki M, Pisal S, Singh J (2012) Feature-based opinion mining and ranking. J Comput Syst Sci 78(4):1175–1184MathSciNetCrossRef

Esuli A, Sebastiani F (2006) Sentiwordnet: A publicly available lexical resource for opinion mining. In: Proceedings of LREC, Vol. 6. Citeseer, pp 417–422

Fei G, Liu B, Hsu M, Castellanos M, Ghosh R (2012) A dictionary-based approach to identifying aspects im-plied by adjectives for opinion mining. In: 24th international conference on computational linguistics, p 309

Feldman R (2013) Techniques and applications for sentiment analysis. Commun ACM 56(4):82–89CrossRef

Hai Z, Chang K, Cong G (2012) One seed to find them all: mining opinion features via association. In: Proceedings of the 21st ACM international conference on information and knowledge management. ACM, pp 255–264

Hai Z, Chang K, Kim J-J (2011) Implicit feature identification via co-occurrence association rule mining. In: Computational linguistics and intelligent text processing. Springer, pp 393–404

Hai Z, Chang K, Kim J-J, Yang CC (2014) Identifying features in opinion mining via intrinsic and extrinsic domain relevance. IEEE Trans Knowl Data Eng 26(3):623–634CrossRef

Htay SS, Lynn KT (2013) Extracting product features and opinion words using pattern knowledge in customer reviews. Sci World J 2013. 394758

Hu M, Liu B (2004a) Mining and summarizing customer reviews. In: Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 168–177. http://dl.acm.org/citation.cfm?id=1014073

Hu M, Liu B (2004b) Mining opinion features in customer reviews. AAAI 4:755–760

Huang S, Liu X, Peng X, Niu Z (2012) Fine-grained product features extraction and categorization in reviews opinion mining. In: 2012 IEEE 12th international conference on IEEE data mining workshops (ICDMW), pp 680–686

Jakob N, Gurevych I (2010) Extracting opinion targets in a single-and cross-domain setting with conditional random fields. In: Proceedings of the 2010 conference on empirical methods in natural language processing. Association for computational linguistics, pp 1035–1045

Jiang P, Zhang C, Fu H, Niu Z, Yang Q (2010) An approach based on tree kernels for opinion mining of online product reviews. In: 2010 IEEE 10th international conference on IEEE data mining (ICDM), pp 256–265

Jin W, Ho HH (2009) A novel lexicalized hmm-based learning framework for web opinion mining. In: Proceedings of the 26th annual international conference on machine learning. Citeseer, pp 465–472

Jin W, Ho HH, Srihari RK (2009) Opinionminer: a novel machine learning system for web opinion mining and extraction. In: Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 1195–1204. http://dl.acm.org/citation.cfm?id=1557148

Kobayashi N, Inui K, Matsumoto Y (2007) Extracting aspect-evaluation and aspect-of relations in opinion mining. In: EMNLP-CoNLL. Citeseer, pp 1065–1074

Li F, Han C, Huang M, Zhu X, Xia Y-J, Zhang S, Yu H (2010) Structure-aware review mining and summarization. In: Proceedings of the 23rd international conference on computational linguistics. Association for computational linguistics, pp 653–661. http://dl.acm.org/citation.cfm?id=1873855

Li S, Wang R, Zhou G (2012) Opinion target extraction using a shallow semantic parsing framework. In: Twenty-sixth AAAI conference on artificial intelligence

Li Y, Qin Z, Xu W, Guo J (2015) A holistic model of mining product aspects and associated sentiments from online reviews. Multimed Tools and Appl 74(23):10177–10194CrossRef

Li Y, Wang H, Qin Z, Xu W, Guo J (2014) Confidence estimation and reputation analysis in aspect extraction. In: 2014 22nd international conference on IEEE pattern recognition (ICPR), pp 3612–3617

Li Z, Zhang M, Ma S, Zhou B, Sun Y (2009) Automatic extraction for product feature words from comments on the web. In: Information retrieval technology. Springer, pp 112–123

Liu B (2010) Sentiment analysis and subjectivity. Handb Nat Lang Process 2:627–666

Liu B (2012) Sentiment analysis and opinion mining. Synth Lect Human Lang Technol 5(1):1–167CrossRef

Liu B, Hsu W, Ma Y (1998) Integrating classification and association rule mining. In: Proceedings of the 4th internatinal conference on knowledge discovery and data mining (KDD)

Liu B, Hu M, Cheng J (2005) Opinion observer: analyzing and comparing opinions on the web. In: Proceedings of the 14th international conference on World Wide Web. ACM, pp 342–351

Liu B, Zhang L (2012) A survey of opinion mining and sentiment analysis. In: Mining text data. Springer, pp 415–463

Liu K, Xu L, Liu Y, Zhao J (2013a) Opinion target extraction using partially-supervised word alignment model. In: Proceedings of the twenty-third international joint conference on artificial intelligence. AAAI Press, pp 2134–2140

Liu K, Xu L, Zhao J (2012) Opinion target extraction using word-based translation model. In: Proceedings of the 2012 joint conference on empirical methods in natural language processing and computational natural language learning. Association for computational linguistics, pp 1346–1356

Liu K, Xu L, Zhao J (2013b) Syntactic patterns versus word alignment: extracting opinion targets from online reviews. In: ACL (1), pp 1754–1763

Liu K, Xu L, Zhao J (2015) Co-extracting opinion targets and opinion words from online reviews based on the word alignment model. IEEE Trans Knowl Data Eng 27(3):636–650MathSciNetCrossRef

Ma B, Zhang D, Yan Z, Kim T (2013) An lda and synonym lexicon based approach to product feature extraction from online consumer product reviews. J Electron Commer Res 14(4):304–314

Marrese-Taylor E, Velasquez J, Bravo-Marquez F (2013a) Opinion zoom: A modular tool to explore tourism opinions on the web. In: 2013 IEEE/WIC/ACM international joint conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), Vol. 3, pp 261–264. http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6690738

Marrese-Taylor E, Velásquez JD, Bravo-Marquez F (2014) A novel deterministic approach for aspect-based opinion mining in tourism products reviews. Expert Syst Appl 41(17):7764–7775CrossRef

Marrese-Taylor E, Velásquez JD, Bravo-Marquez F, Matsuo Y (2013b) Identifying customer preferences about tourism products using an aspect-based opinion mining approach. Proc Comput Sci 22:182–191CrossRef

Medhat W, Hassan A, Korashy H (2014) Sentiment analysis algorithms and applications: a survey. Ain Shams Eng J 5(4):1093–1113CrossRef

Meng X, Wang H (2009) Mining user reviews: from specification to summarization. In: Proceedings of the ACL-IJCNLP 2009 conference short papers. Association for computational linguistics, pp 177–180

Miller G, Fellbaum C (1998) Wordnet: an electronic lexical database. MIT Press, Cambridge

Moghaddam S, Ester M (2010) Opinion digger: an unsupervised opinion miner from unstructured product reviews. In: Proceedings of the 19th ACM international conference on information and knowledge management. ACM, pp 1825–1828

Montoyo A, MartNez-Barco P, Balahur A (2012) Subjectivity and sentiment analysis: an overview of the current state of the area and envisaged developments. Decis Support Syst 53(4):675–679CrossRef

Orimaye SO, Alhashmi SM, Siew E-G (2015) Performance and trends in recent opinion retrieval techniques. Knowl Eng Rev 30(01):76–105CrossRef

Pang B, Lee L (2008) Opinion mining and sentiment analysis. Found Trends Inf Retr 2(1–2):1–135CrossRef

Pang B, Lee L, Vaithyanathan S (2002) Thumbs up?: sentiment classification using machine learning techniques. In: Proceedings of the ACL-02 conference on empirical methods in natural language processing-volume 10. Association for computational linguistics, pp 79–86

Popescu A-M, Etzioni O (2007) Extracting product features and opinions from reviews. In: Natural language processing and text mining. Springer, pp 9–28. http://link.springer.com/chapter/10.1007/978-1-84628-754-1_2

Poria S, Cambria E, Ku L-W, Gui C, Gelbukh A (2014) A rule-based approach to aspect extraction from product reviews. In: Proceedings of the second workshop on natural language processing for social media (SocialNLP), pp 28–37

Qiu G, Liu B, Bu J, Chen C (2009) Expanding domain sentiment lexicon through double propagation. IJCAI 9:1199–1204

Qiu G, Liu B, Bu J, Chen C (2011) Opinion word expansion and target extraction through double propagation. Comput Linguist 37(1): 9–27. http://www.mitpressjournals.org/doi/abs/10.1162/coli_a_00034

Quan C, Ren F (2014) Unsupervised product feature extraction for feature-oriented opinion determination. Inf Sci 272:16–28. http://www.sciencedirect.com/science/article/pii/S0020025514001698

Raju S, Pingali P, Varma V (2009) An unsupervised approach to product attribute extraction. In: Advances in information retrieval. Springer, pp 796–800

Samha AK, Li Y, Zhang J (2014) Aspect-based opinion extraction from customer reviews. arXiv preprint arXiv:1404.1982

Schouten K, Frasincar F (2014) Finding implicit features in consumer reviews for sentiment analysis. In: Web engineering. Springer, pp 130–144

Strapparava C, Valitutti A et al (2004) Wordnet affect: an affective extension of wordnet. In: LREC, vol. 4. pp 1083–1086

Su Q, Xu X, Guo H, Guo Z, Wu X, Zhang X, Swen B, Su Z (2008) Hidden sentiment association in chinese web opinion mining. In: Proceedings of the 17th international conference on World Wide Web. ACM, pp 959–968

Sun L, Li S, Li J, Lv J (2014) A novel context-based implicit feature extracting method. In: 2014 international Conference on IEEE data science and advanced analytics (DSAA), pp 420–424

Tsytsarau M, Palpanas T (2012) Survey on mining subjective data on the web. Data Min Knowl Discov 24(3):478–514CrossRefMATH

Turney PD (2002) Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the 40th annual meeting on association for computational linguistics. Association for computational linguistics, pp 417–424

Wang B, Wang H (2008) Bootstrapping both product features and opinion words from chinese customer reviews with cross-inducing. In: IJCNLP, pp 289–295

Wang W, Xu H, Huang X (2013a) Implicit feature detection via a constrained topic model and svm. In: EMNLP, pp 903–907

Wang W, Xu H, Wan W (2013b) Implicit feature identification via hybrid association rule mining. Expert Syst Appl 40(9):3518–3531CrossRef

Wei C-P, Chen Y-M, Yang C-S, Yang CC (2010) Understanding what concerns consumers: a semantic approach to product feature extraction from consumer reviews. Inf Syst E-Business Manag 8(2):149–167CrossRef

Wiebe JM, Bruce RF, O’Hara TP (1999) Development and use of a gold-standard data set for subjectivity classifications. In: Proceedings of the 37th annual meeting of the association for computational linguistics on computational linguistics. Association for computational linguistics, pp 246–253

Wu Y, Zhang Q, Huang X, Wu L (2009) Phrase dependency parsing for opinion mining. In: Proceedings of the 2009 conference on empirical methods in natural language processing, Volume 3. Association for computational linguistics, pp 1533–1541. http://dl.acm.org/citation.cfm?id=1699700

Xu H, Zhang F, Wang W (2015) Implicit feature identification in chinese reviews using explicit topic mining model. Knowl Based Syst 76:166–175CrossRef

Xu L, Liu K, Lai S, Chen Y, Zhao J (2013a) Mining opinion words and opinion targets in a two-stage framework. In: ACL (1), pp 1764–1773

Xu L, Liu K, Lai S, Chen Y, Zhao J (2013b) Walk and learn: a two-stage approach for opinion words and opinion targets co-extraction. In: Proceedings of the 22nd international conference on World Wide Web companion. International World Wide Web conferences steering committee, pp 95–96

Yan Z, Xing M, Zhang D, Ma B (2015) Exprs: An extended pagerank method for product feature extraction from online consumer reviews. Inf Manag 52(7):850–858CrossRef

Yang B, Cardie C (2013) Joint inference for fine-grained opinion extraction. In: ACL (1), pp 1640–1649

Yu J, Zha Z-J, Wang M, Chua T-S (2011a) Aspect ranking: identifying important product aspects from online consumer reviews. In: Proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies-Volume 1. Association for computational linguistics, pp 1496–1505. http://dl.acm.org/citation.cfm?id=2002654

Yu J, Zha Z-J, Wang M, Wang K, Chua T-S (2011b) Domain-assisted product aspect hierarchy generation: towards hierarchical organization of unstructured consumer reviews. In: Proceedings of the conference on empirical methods in natural language processing. Association for computational linguistics, pp 140–150

Zeng L, Li F (2013) A classification-based approach for implicit feature identification. In: Chinese computational linguistics and natural language processing based on naturally annotated big data. Springer, pp 190–202

Zhang L, Liu B (2014) Aspect and entity extraction for opinion mining. In: Data mining and knowledge discovery for big data. Springer, pp 1–40

Zhang L, Liu B, Lim SH, O’Brien-Strain E (2010) Extracting and ranking product features in opinion documents. In: Proceedings of the 23rd international conference on computational linguistics: posters. Association for computational linguistics, pp 1462–1470. http://dl.acm.org/citation.cfm?id=1944733

Zhang Y, Zhu W (2013) Extracting implicit features in online customer reviews for opinion mining. In: Proceedings of the 22nd international conference on World Wide Web companion. International World Wide Web Conferences steering committee, pp 103–104

Zhao Q, Wang H, Lv P, Zhang C (2014) A bootstrapping based refinement framework for mining opinion words and targets. In: Proceedings of the 23rd ACM international conference on conference on information and knowledge management. ACM, pp 1995–1998

Zhu J, Wang H, Zhu M, Tsou BK, Ma M (2011) Aspect-based opinion polling from customer reviews. IEEE Trans Affect Comput 2(1):37–49CrossRef

Titel: Aspect extraction in sentiment analysis: comparative analysis and survey
verfasst von: Toqir A. Rana
Yu-N Cheah
Publikationsdatum: 01.12.2016
Verlag: Springer Netherlands
Erschienen in: Artificial Intelligence Review / Ausgabe 4/2016
Print ISSN: 0269-2821
Elektronische ISSN: 1573-7462
DOI: https://doi.org/10.1007/s10462-016-9472-z

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