Skip to main content
SpringerLink
Log in

Aspect extraction in sentiment analysis: comparative analysis and survey

  • Published:
Artificial Intelligence Review Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Notes

  1. www.amazon.com

  2. www.cs.uic.edu/~liub/FBS/sentiment-analysis.html

References

  • 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 22:142–151

    Article  Google Scholar 

  • 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 52:201–213

    Article  Google Scholar 

  • 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 3:993–1022

    MATH  Google Scholar 

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

    Article  Google Scholar 

  • 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):9588–9601

    Article  Google Scholar 

  • 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):3174–3184

    Article  Google Scholar 

  • 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):1175–1184

    Article  MathSciNet  Google Scholar 

  • 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):82–89

    Article  Google Scholar 

  • 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):623–634

    Article  Google Scholar 

  • 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

    Google Scholar 

  • 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):10177–10194

    Article  Google Scholar 

  • 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

    Google Scholar 

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

    Article  Google Scholar 

  • 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):636–650

    Article  MathSciNet  Google Scholar 

  • 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

    Google Scholar 

  • 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):7764–7775

    Article  Google Scholar 

  • 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 22:182–191

    Article  Google Scholar 

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

    Article  Google Scholar 

  • 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):675–679

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • 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

    Google Scholar 

  • 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):478–514

    Article  MATH  Google Scholar 

  • 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):3518–3531

    Article  Google Scholar 

  • 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):149–167

    Article  Google Scholar 

  • 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 76:166–175

    Article  Google Scholar 

  • 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):850–858

    Article  Google Scholar 

  • 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):37–49

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Sciences, Universiti Sains Malaysia (USM), 11800, Penang, Malaysia

    Toqir A. Rana & Yu-N Cheah

Authors
  1. Toqir A. Rana
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yu-N Cheah
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Toqir A. Rana.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rana, T.A., Cheah, YN. Aspect extraction in sentiment analysis: comparative analysis and survey. Artif Intell Rev 46, 459–483 (2016). https://doi.org/10.1007/s10462-016-9472-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10462-016-9472-z

Keywords

Search

Navigation