Skip to main content
SpringerLink
Log in

A survey on classification techniques for opinion mining and sentiment analysis

  • Published:
Artificial Intelligence Review Aims and scope Submit manuscript

Abstract

Opinion mining is considered as a subfield of natural language processing, information retrieval and text mining. Opinion mining is the process of extracting human thoughts and perceptions from unstructured texts, which with regard to the emergence of online social media and mass volume of users’ comments, has become to a useful, attractive and also challenging issue. There are varieties of researches with different trends and approaches in this area, but the lack of a comprehensive study to investigate them from all aspects is tangible. In this paper we represent a complete, multilateral and systematic review of opinion mining and sentiment analysis to classify available methods and compare their advantages and drawbacks, in order to have better understanding of available challenges and solutions to clarify the future direction. For this purpose, we present a proper framework of opinion mining accompanying with its steps and levels and then we completely monitor, classify, summarize and compare proposed techniques for aspect extraction, opinion classification, summary production and evaluation, based on the major validated scientific works. In order to have a better comparison, we also propose some factors in each category, which help to have a better understanding of advantages and disadvantages of different methods.

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
Fig. 9
Fig. 10

Similar content being viewed by others

Notes

  1. https://www.twitter.com/.

  2. https://www.facebook.com/.

  3. http://www.amazon.com/.

  4. http://www.yelp.com/.

  5. http://www.tripadvisor.com/.

  6. http://www.cs.cornell.edu/people/pabo/movie-review-data/.

  7. http://www.cs.cornell.edu/people/pabo/movie-review-data/ (review corpus version 2.0).

  8. http://www.cs.jhu.edu/~mdredze/datasets/sentiment/.

  9. www.IMDB.com.

  10. http://wordnet.princeton.edu/.

References

  • Abdul-Mageed M, Diab M, Kübler S (2014) SAMAR: subjectivity and sentiment analysis for arabic social media. Comput Speech Lang 28(1):20–37

    Google Scholar 

  • Acampora G, Cosma G (2015) A comparison of fuzzy approaches to E-commerce review rating prediction. In: 2015 conference of the international fuzzy systems association and the European society for fuzzy logic and technology (IFSA-EUSFLAT-15). Atlantis Press

  • Ahmed S, Danti A (2016) Effective sentimental analysis and opinion mining of web reviews using rule based classifiers. In: Behera H, Mohapatra D (eds) Computational intelligence in data mining—volume 1. Advances in intelligent systems and computing, vol 410. Springer, New Delhi

    Google Scholar 

  • Alfaro C, Cano-Montero J, Gómez J, Moguerza JM, Ortega F (2016) A multi-stage method for content classification and opinion mining on weblog comments. Ann Oper Res 236(1):197–213

    MATH  Google Scholar 

  • Anjaria M, Guddeti RMR (2014) A novel sentiment analysis of social networks using supervised learning. Soc Netw Anal Min 4(1):1–15

    Google Scholar 

  • Appel O, Chicalana F, Carter J, Fujita H (2016) A hybrid approach to the sentiment analysis problem at the sentence level. Knowl Based Syst 108:110–124

    Google Scholar 

  • Arab M, Sohrabi MK (2017) Proposing a new clustering method to detect phishing websites. Turk J Electr Eng Comput Sci. https://doi.org/10.3906/elk-1612-279

    Google Scholar 

  • Archambault D, Greene D, Cunningham P (2013) Twittercrowds: techniques for exploring topic and sentiment in microblogging data. Preprint. arXiv:1306.3839

  • Baeza-Yates R, Ribeiro-Neto B (1999) Modern information retrieval, vol 463. ACM Press, New York

    Google Scholar 

  • Bajpai R, Poria S, Ho D, Cambria E (2017) Developing a concept-level knowledge base for sentiment analysis in Singlish. Preprint. arXiv:1707.04408

  • Balahur A, Perea-Ortega JM (2015) Sentiment analysis system adaptation for multilingual processing: the case of tweets. Inf Process Manag 51(4):547–556

    Google Scholar 

  • Balahur A, Hermida JM, Montoyo A (2012) Detecting implicit expressions of emotion in text: a comparative analysis. Decis Support Syst 53(4):742–753

    Google Scholar 

  • Balazs JA, Velasquez JD (2016) Opinion mining and information fusion: a survey. Inf Fusion 27:95–110

    Google Scholar 

  • Bastı E, Kuzey C, Delen D (2015) Analyzing initial public offerings’ short-term performance using decision trees and SVMs. Decis Support Syst 73:15–27

    Google Scholar 

  • Belkin M, Niyogi P, Sindhwani V (2006) Manifold regularization: a geometric framework for learning from labeled and unlabeled examples. J Mach Learn Res 7:2399–2434

    MathSciNet  MATH  Google Scholar 

  • Berger AL, Pietra VJD, Pietra SAD (1996) A maximum entropy approach to natural language processing. Comput Linguist 22(1):39–71

    Google Scholar 

  • Bilal M, Israr H, Shahid M, Khan A (2016) Sentiment classification of Roman-Urdu opinions using Naïve Bayesian, decision tree and KNN classification techniques. J King Saud Univ Comput Inf Sci 28:330–344

    Google Scholar 

  • Bing L, Chan KC, Ou C (2014) Public sentiment analysis in Twitter data for prediction of a company’s stock price movements. In: IEEE 11th international conference on e-business engineering (ICEBE), pp 232–239

  • Bisio F, Meda C, Gastaldo P, Zunino R, Cambria E (2017) Concept-level sentiment analysis with SenticNet. In: Cambria E, Das D, Bandyopadhyay S, Feraco A (eds) A practical guide to sentiment analysis. Socio-affective computing, vol 5. Springer, Cham, pp 173–188

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

    MATH  Google Scholar 

  • Blitzer J, Dredze M, Pereira F (2007) Biographies, bollywood, boom-boxes and blenders: domain adaptation for sentiment classification. In: ACL, vol 7, pp 440–447

  • Blum A, Chawla S (2001) Learning from labeled and unlabeled data using graph mincuts. In: Brodley CE, Danyluk AP (eds) Proceedings of the eighteenth international conference on machine learning, pp 19–26

  • Blum A, Mitchell T (1998) Combining labeled and unlabeled data with co-training. In: Proceedings of the eleventh annual conference on computational learning theory. ACM, pp 92–100

  • Boiy E, Moens MF (2009) A machine learning approach to sentiment analysis in multilingual Web texts. Inf Retr 12(5):526–558

    Google Scholar 

  • Bollen J, Mao H, Zeng X (2011) Twitter mood predicts the stock market. J Comput Sci 2(1):1–8

    Google Scholar 

  • Bouadjenek MR, Hacid H, Bouzeghoub M (2016) Social networks and information retrieval, how are they converging? A survey, a taxonomy and an analysis of social information retrieval approaches and platforms. Inf Syst 56:1–18

    Google Scholar 

  • Bravo-Marquez F, Frank E, Pfahringer B (2016) Building a Twitter opinion lexicon from automatically-annotated tweets. Knowl Based Syst 108:65–78

    Google Scholar 

  • Cai C, Xia B (2015) Convolutional neural networks for multimedia sentiment analysis. In: 4th Springer conference on natural language processing and Chinese computing, pp 159–167

  • Cambria E (2013) An introduction to concept-level sentiment analysis. In: MICAI 2013: Advances in soft computing and its applications Mexican international conference on artificial intelligence, pp 478–483

  • Cambria E (2016) Affective computing and sentiment analysis. IEEE Intell Syst 31(2):102–107

    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

    Google Scholar 

  • Carter D, Inkpen D (2015) Inferring aspect-specific opinion structure in product reviews using co-training. In: Gelbukh A (ed) Computational linguistics and intelligent text processing. CICLing 2015. Lecture notes in computer science, vol 9042. Springer, Cham, pp 225–240

  • Chao AFY, Yang H (2018) Using Chinese radical parts for sentiment analysis and domain-dependent seed set extraction. Comput Speech Lang 47:194–213

    Google Scholar 

  • Chen LS, Liu CH, Chiu HJ (2011) A neural network based approach for sentiment classification in the blogosphere. J Informetr 5(2):313–322

    Google Scholar 

  • Chen L, Wang F, Qi L, Liang F (2014) Experiment on sentiment embedded comparison interface. Knowl Based Syst 64:44–58

    Google Scholar 

  • Chen T, Xu R, He Y, Xia Y, Wang X (2016) Learning user and product distributed representations using a sequence model for sentiment analysis. IEEE Comput Intell Mag 11(3):34–44

    Google Scholar 

  • Chinsha TC, Joseph S (2015) A syntactic approach for aspect based opinion mining. In: IEEE international conference on semantic computing (ICSC), pp 24–31

  • Claypo N, Jaiyen S (2015) Opinion mining for thai restaurant reviews using K-means clustering and MRF feature selection. In: 7th international conference on knowledge and smart technology (KST), pp 105–108

  • Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297

    MATH  Google Scholar 

  • Cover TM, Thomas JA (2012) Elements of information theory. Wiley, London

    MATH  Google Scholar 

  • Da Silva NFF, Coletta LF, Hruschka ER, Hruschka ER Jr (2016) Using unsupervised information to improve semi-supervised tweet sentiment classification. Inf Sci 355:348–365

    Google Scholar 

  • Dang Y, Zhang Y, Chen H (2010) A lexicon-enhanced method for sentiment classification: an experiment on online product reviews. IEEE Intell Syst 25(4):46–53

    Google Scholar 

  • Daud A, Khan W, Che D (2017) Urdu language processing: a survey. Artif Intell Rev 47(3):279–311

    Google Scholar 

  • Dave K, Lawrence S, Pennock DM (2003) Mining the peanut gallery: opinion extraction and semantic classification of product reviews. In: Proceedings of the 12th international ACM conference on World Wide Web, pp 519–528

  • De A, Kopparapu SK (2013) Unsupervised clustering technique to harness ideas from an Ideas Portal. In: International IEEE conference on advances in computing, communications and informatics (ICACCI), pp 1563–1568

  • De Fortuny EJ, De Smedt T, Martens D, Daelemans W (2014) Evaluating and understanding text-based stock price prediction models. Inf Process Manag 50(2):426–441

    Google Scholar 

  • Di Caro L, Grella M (2013) Sentiment analysis via dependency parsing. Comput Stand Interfaces 35(5):442–453

    Google Scholar 

  • Duncan B, Zhang Y (2015) Neural networks for sentiment analysis on Twitter. In: IEEE 14th international conference on cognitive informatics & cognitive computing (ICCICC), pp 275–278

  • Duwairi RM, Qarqaz I (2014) Arabic sentiment analysis using supervised classification. In: International IEEE conference on future internet of things and cloud (FiCloud), pp 579–583

  • Ebrahimi M, Suen CY, Ormandjieva O (2016) Detecting predatory conversations in social media by deep convolutional neural networks. Digit Investig 18:33–49

    Google Scholar 

  • Farra N, Challita E, Assi RA, Hajj H (2010) Sentence-level and document-level sentiment mining for arabic texts. In: Proceedings of IEEE international conference on data mining workshops, pp 1114–1119

  • Feng S, Song K, Wang D, Yu G (2015) A word-emoticon mutual reinforcement ranking model for building sentiment lexicon from massive collection of microblogs. World Wide Web 18(4):949–967

    Google Scholar 

  • Fernández-Gavilanes M, Álvarez-López T, Juncal-Martínez J, Costa-Montenegro E, González-Castaño FJ (2016) Unsupervised method for sentiment analysis in online texts. Expert Syst Appl 58:57–75

    Google Scholar 

  • Fersini E, Messina E, Pozzi FA (2016) Expressive signals in social media languages to improve polarity detection. Inf Process Manag 52(1):20–35

    Google Scholar 

  • Ficamos P, Liu Y, Chen W (2017) A Naive Bayes and maximum entropy approach to sentiment analysis: capturing domain-specific data in Weibo. In: IEEE international conference on big data and smart computing (BigComp), pp 336–339

  • Gao W, Li S, Xue Y, Wang M, Zhou G (2014) Semi-supervised sentiment classification with self-training on feature subspaces. In: Su X, He T (eds) Chinese Lexical Semantics. CLSW 2014. Lecture notes in computer science, vol 8922. Springer, Cham, pp 231–239

  • Gao K, Xu H, Wang J (2015) A rule-based approach to emotion cause detection for Chinese micro-blogs. Expert Syst Appl 42(9):4517–4528

    Google Scholar 

  • Garcia-Pablos A, Guadros M, Rigau G (2017) W2VLDA: almost unsupervised system for aspect based sentiment analysis. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2017.08.049

    Google Scholar 

  • Goel A, Gautam J, Kumar S (2016) Real time sentiment analysis of tweets using Naive Bayes. In: 2nd international conference on next generation computing technologies (NGCT), pp 257–261

  • Grefenstette G, Qu Y, Shanahan JG, Evans DA (2004) Coupling niche browsers and affect analysis for an opinion mining application. In: Proceedings of RIAO ’04 Coupling approaches, coupling media and coupling languages for information retrieval, pp 186–194

  • Gu X, Gu Y, Wu H (2017) Cascaded convolutional neural networks for aspect-based opinion summary. Neural Process Lett 46:1–20

    Google Scholar 

  • Gupta E, Rathee G, Kumar P, Chauhan DS (2015) Mood swing analyser: a dynamic sentiment detection approach. Proc Natl Acad Sci India Sect A Phys Sci 85(1):149–157

    Google Scholar 

  • Habernal I, Ptáček T, Steinberger J (2015) Supervised sentiment analysis in Czech social media. Inf Process Manag 51(4):532–546

    Google Scholar 

  • Hajmohammadi MS, Ibrahim R, Selamat A (2014) Cross-lingual sentiment classification using multiple source languages in multi-view semi-supervised learning. Eng Appl Artif Intell 36:195–203

    Google Scholar 

  • Hajmohammadi MS, Ibrahim R, Selamat A (2015) Graph-based semi-supervised learning for cross-lingual sentiment classification. In: Guyen N, Trawiński B, Kosala R (eds) Intelligent Information and Database Systems. ACIIDS 2015. Lecture notes in computer science, vol 9011. Springer, Cham, pp 97–106

  • Hasan KMA, Sabuj MS, Afrin Z (2015) Opinion mining using Naïve Bayes. In: IEEE International WIE conference on electrical and computer engineering (WIECON-ECE)

  • Hassan A, Radev D (2010) Identifying text polarity using random walks. In: Proceedings of the 48th annual meeting of the association for computational linguistics. Association for Computational Linguistics, pp 395–403

  • Hatzivassiloglou V, McKeown KR (1997) Predicting the semantic orientation of adjectives. In: Proceedings of the 35th annual meeting of the association for computational linguistics and eighth conference of the European chapter of the association for computational linguistics. Association for Computational Linguistics, pp 174–181

  • He Y, Zhou D (2011) Self-training from labeled features for sentiment analysis. Inf Process Manag 47(4):606–616

    Google Scholar 

  • Hofmann T (1999) Probabilistic latent semantic indexing. In: Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval. ACM, pp 50–57

  • Hong S, Lee J, Lee JH (2014) Competitive self-training technique for sentiment analysis in mass social media. In: 15th international symposium on soft computing and intelligent systems (SCIS), 2014 Joint 7th International Conference on and Advanced Intelligent Systems (ISIS). IEEE, pp 9–12

  • Huang F, Zhang S, Zhang J, Yu G (2017) Multimodal learning for topic sentiment analysis in microblogging. Neurocomputing 253:144–153

    Google Scholar 

  • Hu M, Liu B (2004) Mining and summarizing customer reviews. In: Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 168–177

  • Iosifidis V, Ntutsi E (2017) Large scale sentiment learning with limited labels. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, pp 1823–1832

  • Irsoy O, Cardie C (2014) Opinion mining with deep recurrent neural networks. In: Proceedings of the conference on empirical methods in natural language processing (EMNLP)

  • Jain A, Jain M (2017) Location based Twitter opinion mining using common-sense information. Glob J Enterp Inf Syst 9(2):28–32

    Google Scholar 

  • Jeyapriya A, Selvi K (2015) Extracting aspects and mining opinions in product reviews using supervised learning algorithm. In: 2015 2nd international conference on electronics and communication systems (ICECS). IEEE, pp 548–552

  • Jian Z, Chen X, Wang HS (2010) Sentiment classification using the theory of ANNs. J China Univ Posts Telecommun 17:58–62

    Google Scholar 

  • Jiang D, Luo X, Xuan J, Xu Z (2017a) Sentiment computing for the news event based on the social media big data. IEEE Access 5:2373–2382

    Google Scholar 

  • Jiang M, Wang J, Lan M, Wu Y (2017b) An effective gated and attention-based neural network model for fine-grained financial target-dependent sentiment analysis. In: Springer international conference on knowledge science, engineering and management, pp 42–54

  • Jiménez-Zafra SM, Martín-Valdivia MT, Martínez-Cámara E, Ureña-López LA (2015) Combining resources to improve unsupervised sentiment analysis at aspect-level. J Inf Sci 42:213–229

    Google Scholar 

  • 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, pp 1195–1204

  • Joachims T (1998) Text categorization with support vector machines: learning with many relevant features. Springer, Berlin, pp 137–142

    Google Scholar 

  • Joachims T (2003) Transductive learning via spectral graph partitioning. In: ICML, vol 3, pp 290–297

  • Kagan V, Stevens A, Subrahmanian VS (2015) Using twitter sentiment to forecast the 2013 pakistani election and the 2014 indian election. IEEE Intell Syst 1:2–5

    Google Scholar 

  • Kalchbrenner N, Grefenstette E, Blunsom P (2014) A convolutional neural network for modelling sentences. In: Proceedings of the 52nd annual meeting of the association for computational linguistics

  • Kanayama H, Nasukawa T (2006) Fully automatic lexicon expansion for domain oriented sentiment analysis. In: Proceedings of the conference on empirical methods in natural language processing, Association for Computational Linguistics, pp 355–363

  • Keshavarz H, Abadeh MS (2017) ALGA: adaptive lexicon learning using genetic algorithm for sentiment analysis of microblogs. Knowl Based Syst 122:1–16

    Google Scholar 

  • Keshtkar F, Inkpen D (2013) A bootstrapping method for extracting paraphrases of emotion expressions from texts. Comput Intell 29(3):417–435

    MathSciNet  Google Scholar 

  • Khan FH, Bashir S, Qamar U (2014) TOM: Twitter opinion mining framework using hybrid classification scheme. Decis Support Syst 57:245–257

    Google Scholar 

  • Khan FH, Qamar U, Bashir S (2016) Multi-objective model selection (MOMS)-based semi-supervised framework for sentiment analysis. Cogn Comput 8(4):614–628

    Google Scholar 

  • Khan FH, Qamar U, Bashir S (2017) Lexicon based semantic detection of sentiments using expected likelihood estimate smoothed odds ratio. Artif Intell Rev 48(1):113–138

    Google Scholar 

  • Kisioglu P, Topcu YI (2011) Applying Bayesian belief network approach to customer churn analysis: a case study on the telecom industry of Turkey. Expert Syst Appl 38(6):7151–7157

    Google Scholar 

  • Kobayashi N, Inui K, Matsumoto Y (2007) Extracting aspect-evaluation and aspect-of relations in opinion mining. In: EMNLP-CoNLL, vol 7, pp 1065–1074

  • Kranjc J, Smailović J, Podpečan V, Grčar M, Žnidaršič M, Lavrač N (2015) Active learning for sentiment analysis on data streams: methodology and workflow implementation in the ClowdFlows platform. Inf Process Manag 51(2):187–203

    Google Scholar 

  • Kumar S, Morstatter F, Liu H (2014) Twitter data analytics. Springer, Berlin

    Google Scholar 

  • Lafferty J, McCallum A, Pereira FC (2001) Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Proceedings of the eighteenth international conference on machine learning, pp 282–289

  • Li G, Liu F (2012) Application of a clustering method on sentiment analysis. J Inf Sci 38(2):127–139

    Google Scholar 

  • Li G, Liu F (2014) Sentiment analysis based on clustering: a framework in improving accuracy and recognizing neutral opinions. Appl Intell 40(3):441–452

    Google Scholar 

  • Li G, Chang K, Hoi SC (2012) Multiview semi-supervised learning with consensus. IEEE Trans Knowl Data Eng 24(11):2040–2051

    Google Scholar 

  • Li S, Zhou L, Li Y (2015) Improving aspect extraction by augmenting a frequency-based method with web-based similarity measures. Inf Process Manag 51(1):58–67

    Google Scholar 

  • Li Q, Jin Z, Wang C, Zeng DD (2016) Mining opinion summarizations using convolutional neural networks in Chinese microblogging systems. Knowl Based Syst 107:289–300

    Google Scholar 

  • Li Q, Guo X, Bai X (2017) Weekdays or weekends: exploring the impacts of microblog posting patterns on gratification and addiction. Inf Manag 54(5):613–624

    Google Scholar 

  • Liao C, Feng C, Yang S, Huang H (2016) A hybrid method of domain lexicon construction for opinion targets extraction using syntax and semantics. J Comput Sci Technol 31:595–603

    Google Scholar 

  • Liu B (2007) Web data mining: exploring hyperlinks, contents, and usage data. Springer, Berlin

    MATH  Google Scholar 

  • Liu B (2012) Sentiment analysis and opinion mining. Synthesis lectures on human language technologies. Morgan & Calypool Publishers, pp 1–167. https://doi.org/10.2200/S00416ED1V01Y201204HLT016

  • Liu B (2015) Sentiment analysis: mining opinions, sentiments, and emotions. Cambridge University Press, Cambridge

    Google Scholar 

  • Liu B, Zhang L (2012) A Survey of Opinion Mining and Sentiment Analysis. In: Aggarwal C., Zhai C. (eds) Mining text data. Springer, Boston, MA, pp 415–463

  • Liu J, Seneff S, Zue V (2012) Harvesting and summarizing user-generated content for advanced speech-based HCI. IEEE J Sel Top Signal Process 6(8):982–992

    Google Scholar 

  • Liu S, Li F, Li F, Cheng X, Shen H (2013a) Adaptive co-training SVM for sentiment classification on tweets. In: Proceedings of the 22nd ACM international conference on conference on information & knowledge management. ACM, pp 2079–2088

  • Liu S, Zhu W, Xu N, Li F, Cheng XQ, Liu Y, Wang Y (2013b) Co-training and visualizing sentiment evolvement for tweet events. In: Proceedings of the 22nd international conference on World Wide Web companion. International World Wide Web Conferences Steering Committee, pp 105–106

  • Lo SL, Cambria E, Chiong R, Cornforth D (2017) Multilingual sentiment analysis: from formal to informal and scarce resource languages. Artif Intell Rev 48(4):499–527

    Google Scholar 

  • Lu TJ (2015) Semi-supervised microblog sentiment analysis using social relation and text similarity. In: 2015 international conference on big data and smart computing (BigComp). IEEE, pp 194–201

  • Luo W, Zhuang F, Zhao W, He Q, Shi Z (2015) QPLSA: utilizing quad-tuples for aspect identification and rating. Inf Process Manag 51(1):25–41

    Google Scholar 

  • Lv Y, Liu J, Chen H, Mi J, Liu M, Zheng Q (2017) Opinioned post detection in Sina Weibo. IEEE Access 5:7263–7271

    Google Scholar 

  • Ma B, Zhang N, Liu G, Li L, Yuan H (2015) Semantic search for public opinions on urban affairs: a probabilistic topic modeling-based approach. Inf Process Manag 52:430

    Google Scholar 

  • Ma H, Jia M, Zhang D, Lin X (2017) Combining tag correlation and user social relation for microblog recommendation. Inf Sci 385–386:325–337

    Google Scholar 

  • Majumder N, Poria S, Gelbukh A, Cambria E (2017) Deep learning-based document modeling for personality detection from text. IEEE Intell Syst 32(2):74–79

    Google Scholar 

  • Manek AS, Shenoy PD, Mohan MC, Venougopal KR (2017) Aspect term extraction for sentiment analysis in large movie reviews using Gini Index feature selection method and SVM classifier. World Wide Web 20(2):135–154

    Google Scholar 

  • Marcheggiani D, Täckström O, Esuli A, Sebastiani F (2014) Hierarchical multi-label conditional random fields for aspect-oriented opinion mining. In: de Rijke M et al (eds) Advances in information retrieval. ECIR 2014. Lecture notes in computer science, vol 8416. Springer, Cham, pp 273–285

  • 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

    Google Scholar 

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

    Google Scholar 

  • Mele I (2013) Web usage mining for enhancing search-result delivery and helping users to find interesting web content. In: Proceedings of the sixth ACM international conference on Web search and data mining. ACM, pp 765–770

  • Mesnil G, Mikolov T, Ranzato MA, Bengio Y (2015) Ensemble of generative and discriminative techniques for sentiment analysis of movie reviews. Preprint. arXiv:1412.5335

  • Mihalcea R, Banea C, Wiebe JM (2007) Learning multilingual subjective language via cross-lingual projections. In: Proceedings of the Association for Computational Linguistics (ACL 2007), Prague

  • Mohammad SM, Zhu X, Kiritchenko S, Martin J (2015) Sentiment, emotion, purpose, and style in electoral tweets. Inf Process Manag 51(4):480–499

    Google Scholar 

  • Molina-González MD, Martínez-Cámara E, Martín-Valdivia MT, Urena-López LA (2014) Cross-domain sentiment analysis using Spanish opinionated words. In: Métais E, Roche M, Teisseire M (eds) Natural language processing and information systems. NLDB 2014. Lecture notes in computer science, vol 8455. Springer, Cham, pp 214–219

  • Molina-González MD, Martínez-Cámara E, Martín-Valdivia MT, Ureña-López LA (2015) A Spanish semantic orientation approach to domain adaptation for polarity classification. Inf Process Manag 51(4):520–531

    Google Scholar 

  • Moraes R, Valiati JF, Neto WPG (2013) Document-level sentiment classification: an empirical comparison between SVM and ANN. Expert Syst Appl 40(2):621–633

    Google Scholar 

  • Moreo A, Romero M, Castro JL, Zurita JM (2012) Lexicon-based comments-oriented news sentiment analyzer system. Expert Syst Appl 39(10):9166–9180

    Google Scholar 

  • Mudinas A, Zhang D, Levene M (2012) Combining lexicon and learning based approaches for concept-level sentiment analysis. In: Proceedings of the first international workshop on issues of sentiment discovery and opinion mining

  • Muhammad A, Wiratunga M, Lothian R (2016) Contextual sentiment analysis for social media genres. Knowl Based Syst 108:92–101

    Google Scholar 

  • Mukherjee A, Liu B (2012) Aspect extraction through semi-supervised modeling. In: Proceedings of the 50th annual meeting of the association for computational linguistics: long papers—volume 1. Association for Computational Linguistics, pp 339–348

  • Mullen T, Collier N (2004) Sentiment analysis using support vector machines with diverse information sources. In: EMNLP, vol 4, pp 412–418

  • Nofer M, Hinz O (2015) Using Twitter to predict the stock market. Bus Inf Syst Eng 57(4):229–242

    Google Scholar 

  • Olson DL, Delen D (2008) Advanced data mining techniques. Springer, Berlin

    MATH  Google Scholar 

  • Pandarachalil R, Sendhilkumar S, Mahalakshmi GS (2015) Twitter sentiment analysis for large-scale data: an unsupervised approach. Cogn Comput 7(2):254–262

    Google Scholar 

  • Pandey AC, Rajpoot DS, Saraswat M (2017) Twitter sentiment analysis using hybrid cuckoo search method. Inf Process Manag 53(4):764–779

    Google Scholar 

  • Pang B, Lee L (2004) A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts. In: Proceedings of the 42nd annual meeting on Association for Computational Linguistics. Association for Computational Linguistics

  • 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, vol 10, pp 79–86

  • Parveen H, Pandey S (2016) Sentiment analysis on Twitter data-set using Naive Bayes algorithm. In: 2nd international conference on applied and theoretical computing and communication technology (iCATccT), pp 416–419

  • Penalver-Martinez I, Garcia-Sanchez F, Valencia-Garcia R, Rodriguez-Garcia MA, Moreno V, Fraga A, Sanchez-Cervantes JL (2014) Feature-based opinion mining through ontologies. Expert Syst Appl 41(13):5995–6008

    Google Scholar 

  • Peng H, Cambria E, Hussain A (2017) A review of sentiment analysis research in Chinese language. Cogn Comput 9(4):423–435

    Google Scholar 

  • Petz G, Karpowicz M, Fürschuß H, Auinger A, Stříteský V, Holzinger A (2015) Computational approaches for mining user’s opinions on the Web 2.0. Inf Process Manag 51(4):510–519

    Google Scholar 

  • Pham D, Le A (2017) Learning multiple layers of knowledge representation for aspect based sentiment analysis. Data Knowl Eng. https://doi.org/10.1016/j.datak.2017.06.001

    Google Scholar 

  • Phu VN, Dat ND, Tran VTN, Chau VTN, Nguyen TA (2017) Fuzzy C-means for english sentiment classification in a distributed system. Appl Intell 46(3):717–738

    Google Scholar 

  • Ponomareva N (2014) Graph-based approaches for semi-supervised and cross-domain sentiment analysis. PhD Thesis, University of Wolverhampton

  • Poria S, Gelbukh A, Hussain A, Howard N, Das D, Bandyopadhyay S (2013) Enhanced SenticNet with affective labels for concept-based opinion mining. IEEE Intell Syt 28(2):31–38

    Google Scholar 

  • Poria S, Cambria E, Winterstein G, Huang GB (2014) Sentic patterns: dependency-based rules for concept-level sentiment analysis. Knowl Based Syst 69:45–63

    Google Scholar 

  • Poria A, Cambria E, Gelbukh A (2016) Aspect extraction for opinion mining with a deep convolutional neural network. Knowl Based Syst 108:42–49

    Google Scholar 

  • Poria S, Peng H, Hussan A, Howard N, Cambria E (2017) Ensemble application of convolutional neural networks and multiple kernel learning for multimodal sentiment analysis. Neurocomputing 261:217–230

    Google Scholar 

  • Qazi A, Syed KBS, Raj RG, Cambria E, Tahir M, Alghazzawi D (2016) A concept-level approach to the analysis of online review helpfulness. Comput Hum Behav 58:75–81

    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

    Google Scholar 

  • Quinlan JR (1986) Induction of decision trees. Mach Learn 1(1):81–106

    Google Scholar 

  • Rabiner LR (1989) A tutorial on hidden Markov models and selected applications in speech recognition. Proc IEEE 77(2):257–286

    Google Scholar 

  • Ramadhani RA, Indirani F, Nugrahadi DT (2016) Comparison of Naive Bayes smoothing methods for Twitter sentiment analysis. In: International conference on advanced computer science and information systems (ICACSIS), pp 287–292

  • Rana TA, Cheah Y (2016) Aspect extraction in sentiment analysis: comparative analysis and survey. Artif Intell Rev 46(4):459–483

    Google Scholar 

  • Rao Y, Lei J, Wenyin L, Li Q, Chen M (2014) Building emotional dictionary for sentiment analysis of online news. World Wide Web 17(4):723–742

    Google Scholar 

  • Rathan M, Hulipalled VR, Venugopal KR, Patnaik LM (2017) Consumer insight mining: aspect based Twitter opinion mining of mobile phone reviews. Appl Soft Comput. https://doi.org/10.1016/j.asoc.2017.07.056

    Google Scholar 

  • Ravi K, Ravi V (2015) A survey on opinion mining and sentiment analysis: Tasks, approaches and applications. Knowl Based Syst 89:14–46

    Google Scholar 

  • Ren F, Kang X (2013) Employing hierarchical Bayesian networks in simple and complex emotion topic analysis. Comput Speech Lang 27(4):943–968

    Google Scholar 

  • Riaz S, Fatima M, Kamran M, Nasir MW (2017) Opinion mining on large scale data using sentiment analysis and k-means clustering. Clust Comput 20:1–16

    Google Scholar 

  • Rout JK, Dalima A, Choo KR, Bakshi S, Jena SK (2017) Revisiting semi-supervised learning for online deceptive review detection. IEEE Access 5:1319–1327

    Google Scholar 

  • Saif H, He Y, Fernandez M, Alani H (2016) Contextual semantics for sentiment analysis of Twitter. Inf Process Manag 52(1):5–19

    Google Scholar 

  • Saleh MR, Martín-Valdivia MT, Montejo-Ráez A, Ureña-López LA (2011) Experiments with SVM to classify opinions in different domains. Expert Syst Appl 38(12):14799–14804

    Google Scholar 

  • Scholer F, Kelly D, Carterette B (2016) Information retrieval evaluation using test collections. Inf Retr J 19(3):225–229

    Google Scholar 

  • Severyn A, Moschitti A, Uryupina O, Plank B, Filippova K (2016) Multi-lingual opinion mining on youtube. Inf Process Manag 52(1):46–60

    Google Scholar 

  • Shah RR, Yu Y, Verma A, Tang S, Shaik AD, Zimmermann R (2016) Leveraging multimodal information for event summarization and concept-level sentiment analysis. Knowl Based Syst 108:102–109

    Google Scholar 

  • Sharma R, Nigam S, Jain R (2014) Opinion mining of movie reviews at document level. Preprint. arXiv:1408.3829

  • Shi B, Chang K (2008) Generating a concept hierarchy for sentiment analysis. In: IEEE international conference on systems, man and cybernetics, SMC 2008. IEEE, pp 312–317

  • Sierra B, Lazkano E, Jauregi E, Irigoien I (2009) Histogram distance-based bayesian network structure learning: a supervised classification specific approach. Decis Support Syst 48(1):180–190

    Google Scholar 

  • Sindhwani V, Melville P (2008) Document-word co-regularization for semi-supervised sentiment analysis. In: 8th IEEE international conference on data mining, pp 1025–1030

  • Singh J, Gupta V (2017) A systematic review of text stemming techniques. Artif Intell Rev 48(2):157–217

    Google Scholar 

  • Sisodia DS, Verma S (2012) Web usage pattern analysis through web logs: a review. In: 2012 international joint conference on computer science and software engineering (JCSSE). IEEE, pp 49–53

  • Sohrabi MK (2018) A gossip-based information fusion protocol for distributed frequent itemset mining. Enterp Inf Syst. https://doi.org/10.1080/17517575.2017.1405286

    Google Scholar 

  • Sohrabi MK, Akbari S (2016) A comprehensive study on the effects of using data mining techniques to predict tie strength. Comput Hum Behav 60:534–541

    Google Scholar 

  • Sohrabi MK, Azgomi H (2017a) Parallel set similarity join on big data based on locality-sensitive hashing. Sci Comput Program 145:1–12

    Google Scholar 

  • Sohrabi MK, Azgomi H (2017b) TSGV: a table-like structure based greedy method for materialized view selection in data warehouse. Turk J Electr Eng Comput Sci 25(4):3175–3187

    Google Scholar 

  • Sohrabi MK, Barforoush AA (2012) Efficient colossal pattern mining in high dimensional datasets. Knowl Based Syst 33:41–52

    Google Scholar 

  • Sohrabi MK, Barforoush AA (2013) Parallel frequent itemset mining using systolic arrays. Knowl Based Syst 37:462–471

    Google Scholar 

  • Sohrabi MK, Ghods V (2014) Top-down vertical itemset mining. In: Proceedings of the SPIE 9443 sixth international conference on graphic and image processing

  • Sohrabi MK, Ghods V (2015) Top- materialized view selection for a data warehouse using frequent itemset mining. In: Proceedings of the ICACTE conference, Berlin, Germany

  • Sohrabi MK, Ghods V (2016) CUSE: a novel cube-based approach for sequential pattern mining. In: Proceedings of the IEEE international symposium on computational business intelligence, Olten, Switzerland

  • Sohrabi MK, Karimi F (2018) Feature selection approach to detect spam in the Facebook social network. Arab J Sci Eng. https://doi.org/10.1007/s13369-017-2855-x

    Google Scholar 

  • Sohrabi MK, Marzooni HH (2016) Association rule mining using new FP-linked list algorithm. J Adv Comput Res 7(01):23–34

    Google Scholar 

  • Sohrabi MK, Roshani R (2017) Frequent itemset mining using cellular learning automata. Comput Hum Behav 68:244–253

    Google Scholar 

  • Sohrabi MK, Tajik A (2017) Multi-objective feature selection for warfarin dose prediction. Comput Biol Chem 69:126–133

    Google Scholar 

  • Speriosu M, Sudan N, Upadhyay S, Baldridge J (2011) Twitter polarity classification with label propagation over lexical links and the follower graph. In: Proceedings of the first workshop on unsupervised learning in NLP. Association for Computational Linguistics, pp 53–63

  • Subrahmanian VS, Reforgiato D (2008) AVA: adjective-verb-adverb combinations for sentiment analysis. IEEE Intell Syst 23(4):43–50

    Google Scholar 

  • Subramanya A, Bilmes J (2011) Semi-supervised learning with measure propagation. J Mach Learn Res 12:3311–3370

    MathSciNet  MATH  Google Scholar 

  • Sun J, Wang G, Cheng X, Fu Y (2015) Mining affective text to improve social media item recommendation. Inf Process Manag 51(4):444–457

    Google Scholar 

  • Sun S, Luo C, Chen J (2017) A review of natural language processing techniques for opinion mining systems. Inf Fusion 36:10–25

    Google Scholar 

  • Taboada M, Brooke J, Tofiloski M, Voll K, Stede M (2011) Lexicon-based methods for sentiment analysis. Comput Linguist 37(2):267–307

    Google Scholar 

  • Talukdar PP, Crammer K (2009) New regularized algorithms for transductive learning. In: Buntine W, Grobelnik M, Mladenić D, Shawe-Taylor J (eds) Machine learning and knowledge discovery in databases. ECML PKDD 2009. Lecture notes in computer science, vol 5782. Springer, Berlin, pp 442–457

  • Tang D, Qin B, Liu T, Yang Y (2015) User modeling with neural network for review rating prediction. In: Proceedings of IJCAI, pp 1340–1346

  • Tang H, Tan S, Cheng X (2009) A survey on sentiment detection of reviews. Expert Syst Appl 36(7):10760–10773

    Google Scholar 

  • Tripathy A, Agrawal A, Rath SK (2016) Classification of sentiment reviews using n-gram machine learning approach. Expert Syst Appl 57:117–126

    Google Scholar 

  • Titov I, McDonald R (2008) Modeling online reviews with multi-grain topic models. In: Proceedings of the 17th international conference on World Wide Web, pp 111–120

  • Tsagkalidou K, Koutsonikola V, Vakali A, Kafetsios K (2011) Emotional aware clustering on micro-blogging sources. In: D’Mello S, Graesser A, Schuller B, Martin JC (eds) Affective computing and intelligent interaction. ACII 2011. Lecture notes in computer science, vol 6974. Springer, Berlin, pp 387–396

  • Tsai AC, Wu C, Tsai RT, Hsu JY (2013) Building a concept-level sentiment dictionary based on commonsense knowledge. IEEE Intell Syt 28(2):22–30

    Google Scholar 

  • Tsakalidis A, Papadopoulos S, Cristea AI, Kompatsiaris Y (2015) Predicting elections for multiple countries using Twitter and polls. IEEE Intell Syst 30(2):10–17

    Google Scholar 

  • Turney P (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 ACL’02, Association for Computational Linguistics, pp 417–424

  • Unankard S, Li X, Sharaf M, Zhong J, Li X (2014) Predicting elections from social networks based on sub-event detection and sentiment analysis. In: Web information systems engineering—WISE 2014. Springer, Berlin, pp 1–16

  • Vakali A, Kafetsios K (2012) Emotion aware clustering analysis as a tool for Web 2.0 communities detection: implications for curriculum development. In: World Wide Web Conference. WWW

  • Velásquez JD (2013) Combining eye-tracking technologies with web usage mining for identifying Website Keyobjects. Eng Appl Artif Intell 26(5):1469–1478

    Google Scholar 

  • Vilares D, Alonso MA, Gómez-Rodríguez C (2017) Supervised sentiment analysis in multilingual environments. Inf Process Manag 53(3):595–607

    Google Scholar 

  • Vinodhini G, Chandrasekaran RM (2016) A comparative performance evaluation of neural network based approach for sentiment classification of online reviews. J King Saud Univ Comput Inf Sci 28(1):2–12

    Google Scholar 

  • Vulić I, De Smet W, Tang J, Moens MF (2015) Probabilistic topic modeling in multilingual settings: an overview of its methodology and applications. Inf Process Manag 51(1):111–147

    Google Scholar 

  • Wan X (2011) Bilingual co-training for sentiment classification of Chinese product reviews. Comput Linguist 37(3):587–616

    Google Scholar 

  • Wang G, Zhang Z, Sun J, Yang S, Larson CA (2015a) POS-RS: a random subspace method for sentiment classification based on part-of-speech analysis. Inf Process Manag 51(4):458–479

    Google Scholar 

  • Wang J, Cong G, Zhao XW, Li X (2015b) Mining user intents in twitter: a semi-supervised approach to inferring intent categories for tweets. In: Twentyninth AAAI conference on artificial intelligence

  • Wang J, Xue Y, Li S, Zhou G (2015c) Leveraging interactive knowledge and unlabeled data in gender classification with co-training. In: Liu A, Ishikawa Y, Qian T, Nutanong S, Cheema M (eds) Database Systems for Advanced Applications. DASFAA 2015. Lecture notes in computer science, vol 9052. Springer, Cham, pp 246–251

  • Wang G, Zheng D, Yang S (2017a) FCE-SVM: a new cluster based ensemble method for opinion mining from social media. Inf Syst e-Bus Manag 15:1–22

    Google Scholar 

  • Wang W, Tan G, Wang H (2017b) Cross-domain comparison of algorithm performance in extracting aspect-based opinions from Chinese online reviews. Int J Mach Learn Cybern 8(3):1053–1070

    Google Scholar 

  • Wehrmann J, Becker W, Cagnini HE, Barros RC (2017) A character-based convolutional neural network for language-agnostic Twitter sentiment analysis. In: IEEE international joint conference on neural networks (IJCNN), pp 2384–2391

  • Wen S, Wan X (2014) Emotion classification in microblog texts using class sequential rules. In: Twentyeighth AAAI conference on artificial intelligence

  • Wilson T, Wiebe J, Hoffmann P (2005) Recognizing contextual polarity in phrase level sentiment analysis. In: Proceedings of HLT/EMNLP-05

  • 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

  • Wu F, Song Y, Huang Y (2016) Microblog sentiment classification with heterogeneous sentiment knowledge. Inf Sci 373:149–164

    Google Scholar 

  • Xia R, Zong C, Li S (2011) Ensemble of feature sets and classification algorithms for sentiment classification. Inf Sci 181(6):1138–1152

    Google Scholar 

  • Xia Y, Cambria E, Hussain A (2015) AspNet: aspect extraction by bootstrapping generalization and propagation using an aspect network. Cogn Comput 7(2):241–253

    Google Scholar 

  • Xia R, Xu F, Yu J, Qi Y, Cambria E (2016) Polarity shift detection, elimination and ensemble: a three-stage model for document-level sentiment analysis. Inf Process Manag 52(1):36–45

    Google Scholar 

  • Xing FZ, Cambria E, Welsch RE (2018) Natural language based financial forecasting: a survey. Artif Intell Rev. https://doi.org/10.1007/s10462-017-9588-9

    Google Scholar 

  • Xu L, Lin J, Wang L, Yin C, Wang J (2017) Deep convolutional neural network based approach for aspect-based sentiment analysis. Adv Sci Technol Lett 143:199–204

    Google Scholar 

  • Yan X, Huang T (2015) Tibetan sentence sentiment analysis based on the maximum entropy model. In: 10th international conference on broadband and wireless computing, communication and applications (BWCCA), pp 594–597

  • Yan Z, Jiang X, Pedryc W (2017) Fusing and mining opinions for reputation generation. Inf Fusion 36:172–184

    Google Scholar 

  • Yang B, Cardie C (2014) Context-aware learning for sentence-level sentiment analysis with posterior regularization. In: ACL, no 1, pp 325–335

  • Yin PY, Guo YM (2013) Optimization of multi-criteria website structure based on enhanced tabu search and web usage mining. Appl Math Comput 219(24):11082–11095

    Google Scholar 

  • Yu J, Zha ZJ, Wang M, Chua TS (2011) 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, vol 1, pp 1496–1505

  • Zhang X, Gong W, Kawamura Y (2004) Customer behavior pattern discovering with web mining. In: Yu JX, Lin X, Lu H, Zhang Y (eds) Advanced web technologies and applications. APWeb, Lecture notes in computer science, vol 3007. Springer, Berlin, pp 844–853

  • Zhou F, Jiao JR, Yang XJ, Lei B (2017) Augmenting feature model through customer preference mining by hybrid sentiment analysis. Expert Syst Appl 89:306–317

    Google Scholar 

  • Zhuang L, Jing F, Zhu XY (2006) Movie review mining and summarization. In: Proceedings of the 15th ACM international conference on information and knowledge management, pp 43–50

  • Zimmermann M, Ntoutsi E, Spiliopoulou M (2016) Extracting opinionated (sub) features from a stream of product reviews using accumulated novelty and internal re-organization. Inf Sci 329:876–899

    Google Scholar 

  • Zimmermann M, Ntoutsi E, Spiliopoulou M (2014) A semi-supervised self-adaptive classifier over opinionated streams. In: 2014 IEEE international conference on data mining workshop (ICDMW). IEEE, pp 425–432

Download references

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Semnan Branch, Islamic Azad University, Semnan, Iran

    Fatemeh Hemmatian & Mohammad Karim Sohrabi

Authors
  1. Fatemeh Hemmatian
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohammad Karim Sohrabi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohammad Karim Sohrabi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hemmatian, F., Sohrabi, M.K. A survey on classification techniques for opinion mining and sentiment analysis. Artif Intell Rev 52, 1495–1545 (2019). https://doi.org/10.1007/s10462-017-9599-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10462-017-9599-6

Keywords

Search

Navigation