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A review of literature on the use of machine learning methods for opinion mining

Year 2016, Volume 22, Issue 2, 111 - 122, 01.05.2016

Abstract

Opinion mining is an emerging field which uses methods of natural language processing, text mining and computational linguistics to extract subjective information of opinion holders. Opinion mining can be viewed as a classification problem. Hence, machine learning based methods are widely employed for sentiment classification. Machine learning based methods in opinion mining can be mainly classified as supervised, semi-supervised and unsupervised methods. In this study, main existing literature on the use of machine learning methods for opinion mining has been presented. Besides, the weak and strong characteristics of machine learning methods have been discussed.

References

  • Ganesan K, Kim H. D. “Opinion Mining Tutorial (Sentiment Analysis)”.http://www.slideshare.net/KavitaGanesan/op inion-mining-kavitahyunduk00 (15.01.2015).
  • Medhat W, Hassan A, Korashy H. “Sentiment analysis algorithms and applications: a survey”. Ain Shams Engineering Journal, 5(4), 1093-1113, 2014
  • Taboada M, Brooke J, Tofiloski M, Voll K, Stede M. “Lexicon-Based methods for sentiment analysis”. Computational Linguistics, 37(2), 267-307, 2011.
  • Pang B, Lee L, Vaithyanathan S. “Thumbs up?: Sentiment classification using machine learning techniques”. Conference on Empirical Methods in Natural Language Processing (EMNLP), Philadelphia, PA, USA, 6-7 July 2002.
  • Pang B, Lee L. “A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts”. 42nd Annual Meeting of the Association for Computational Linguistics (ACL), Barcelona, Spain, 21-26 July 2004.
  • Whitelaw C, Garg N, Argamon S. “Using appraisal groups for sentiment analysis”. 14th ACM International Conference on Information and Knowledge Management (CIKM), Bremen, Germany, 31 October-5 November 2005.
  • Matsumoto S, Takamura H, Okumura M. “Sentiment classification using word sub-sequences and dependency sub-trees”. 9th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Hanoi, Vietnam, 18- 20 May 2005.
  • McDonald R, Hannan K, Neylon T, Wells M, Reynar J. “Structured models for fine-to-coarse sentiment analysis”. 45th Annual Meeting of the Association of Computational Linguistics, Prague, Czech Republic, 23-30 June 2007.
  • Zaidan OF, Eisner J, Piatko CD. “Using ‘annotator rationales’ to improve machine learning for text categorization”. Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT), Rochester, NY, USA, 22-27 April 2007.
  • Tan S, Zhang J. “An empirical study of sentiment analysis for Chinese document”. Expert Systems with Applications, 34(4), 2622-2629, 2008.
  • Prabowo R, Thelwall M. “Sentiment Analysis: a combined approach”. Journal of Informetrics, 3(2), 143-157, 2009.
  • Yassenalina A, Yue Y, Cardie C. “Multi-Level structured models for document-level sentiment classification”. Conference on Empirical Methods in Natural Language Processing (EMNLP), Boston, MA, USA, 9-11 October 2010.
  • Qui G, He X, Zhang F, Shi Y, Bu J, Chen C. “DASA: dissatisfaction-oriented advertising based on sentiment analysis”. Expert Systems with Application, 37(9), 6182-6191, 2010.
  • Zhao YY, Qin B, Liu T. “Integrating intra-and inter- document evidences for improving sentence sentiment classification”. 1417-1425, 2010. Sinica, 36(10),
  • Bai X. “Predicting consumer sentiments from online text”. Decision Support Systems, 50(4), 732-742, 2011.
  • Chen CC, Tseng YD. “Quality evaluation of product reviews using an information quality framework”. Decision Support Systems, 50(4), 755-768, 2011.
  • Wang S, Li D, Song X, We, Y, Li H. “A feature selection method based on improved fisher’s discriminant ration for text sentiment classification”. Expert Systems with Applications, 38(7), 8696-8702, 2011.
  • Xia R, Zong C, Li S. “Ensemble of feature sets and classification algorithms”. Information Sciences, 181(6), 1138-1152, 2011.
  • Kang H, Yoo SJ, Han M. “Senti-Lexicon and improved Naïve Bayes algorithms for sentiment analysis of restaurant reviews”. Expert Systems with Applications, 39(5), 6000- 6010, 2012.
  • Sun Y, Wong AKC, Kamel SM. “Classification of imbalanced data: a review”. International Journal of Pattern Recognition 687-719, 2009. Intelligence, 23(4),
  • Li YM, Li TY. “Deriving Market intelligence from microblogs”. Decision Support Systems, 55(1), 206-217, 2013.
  • Moraes R, Valiati JF, Neto WPG. “Document-Level sentiment classification: an empirical comparison between SVM and ANN”. Expert Systems with Applications, 40(2), 621-633, 2013.
  • Wang G, Sun J, Ma J, Xu K, Gu J. “Sentiment classification: the contribution of ensemble learning”. Decision Support Systems, 57, 77-93, 2014.
  • Chalothom T, Ellman J. Simple Approaches of Sentiment Analysis via Ensemble Learning. Editor: Kim KJ. Information Science and Applications, 631-639, Berlin, Germany, Springer, 2015.
  • Zheng L, Wang H, Gao S. “Sentimental feature selection for sentiment analysis of Chinese online reviews”. International Journal of Machine Learning and Cybernetics, 1-10, 2015.
  • Lin C. Probabilistic Topic Models for Sentiment Analysis on the Web. PhD Thesis, University of Exeter, Exeter, UK, 2011.
  • Aue A, Gamon M. “Customizing sentiment classifiers to new domains: a case study”. International Conference on Recent Advances in Natural Language Processing (RANLP), Borovets, Bulgaria, 21-23 September 2005.
  • Tan S, Wu G, Tang H, Cheng X. “A novel scheme for domain- transfer problem in the context of sentiment analysis”. Conference on Information and Knowledge Management (CIKM), Lisbon, Portugal, 6-10 November 2007.
  • Blitzer J, Dredze M, Pereira F. “Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification”. 45th Annual Meeting of the Association for Computational Linguistics (ACL), Prague, Czech Republic, 25-27 June 2007.
  • Mihalcea R, Banae C, Wiebe J. “Learning multilingual subjective language via cross-lingual projections”. 45th Annual Meeting of the Association for Computational Linguistics (ACL), Prague, Czech Republic, 25-27 June 2007.
  • Li S, Zong C. “Multi-Domain sentiment classification”. 46th Annual Meeting of the Association for Computational Linguistics (ACL), Columbus, OH, USA, 19-20 June 2008.
  • Banae C, Mihalcea R, Wiebe J. “Multilingual subjectivity analysis using machine translation”. Conference on Empirical Methods in Natural Language Processing (EMNLP), Honolulu, HI, USA, 25-27 October 2008.
  • Li T, Zhang Y, Sindhwani V. “A non-negative matrix tri- factorization approach to sentiment classification with lexical prior knowledge”. 47th Annual Meeting of the Association for Computational Linguistics (ACL), Suntec, Singapore, 2-7 August 2009.
  • Dasgupta S, Ng V. “Mine the easy, classify the hard: a semi- supervised classification”. 47th Annual Meeting of the Association for Computational Linguistics (ACL), Suntec, Singapore, 2-7 August 2009. to automatic sentiment
  • Wan X. “Co-training for cross-lingual sentiment classification”. 47th Annual Meeting of the Association for Computational Linguistics (ACL), Suntec, Singapore, 2-7 August 2009.
  • Li S, Huang CR, Zhou G, Lee SYM. “Employing personal/impersonal views in supervised and semi- supervised sentiment classification”. 48th Annual Meeting of the Association for Computational Linguistics (ACL), Uppsala, Sweden, 11-16 July 2010.
  • He Y, Zhou D. “Self-Training from labelled features for sentiment analysis”. Information Processing and Management, 47(4), 606-616, 2011.
  • He Y, Lin C, Alani H. “Automatically extracting polarity- bearing topics for cross-domain sentiment classification”. 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (ACL-HLT), Portland, OR, USA, 19-24 June 2011.
  • Hernandez OJ, Rodriguez JD, Alzate, L, Lucania M, Inza I, Lozano JA. “Approaching sentiment analysis by using semi-supervised classifiers”. Neurocomputing, 92, 98-115, 2012. of multi-dimensional
  • Hajmohammadi MS, Ibrahim R, Selamat A. “Bi-View semi-supervised active learning for cross-lingual sentiment classification”. Information Processing and Management, 50(5), 718-732, 2014.
  • Hajmohammadi MS, Ibrahim R, Selamat A. “Cross-Lingual sentiment classification using multiple source languages in multi-view semi-supervised learning”. Engineering Applications of Artificial Intelligence, 36, 195-203, 2014.
  • Hajmohammadi MS, Ibrahim R, Selamat A, Fujita H. “Combination of active learning and self-training for cross-lingual sentiment classification with density analysis of unlabelled samples”. Information Sciences, 317, 67-77, 2015.
  • Turney P, Littman M. “Unsupervised learning of semantic orientation from a hundred-billion-word corpus”. Institute for Information Technology, National Research Council, Ottawa, Ontario, Canada, Technical report, ERB- 1094, 2002.
  • Andreevskai A, Bergler S. “When specialists and generalists work together: overcoming domain dependence in sentiment tagging”. 46th Annual Meeting of the Association for Computational Linguistics (ACL), Columbus, OH, USA, 19-20 June 2008.
  • Zagibalov T, Carroll J. “Unsupervised classification of sentiment and objectivity in Chinese text”. International Joint Conference on Natural Language Processing (IJCNLP), Hyderabad, India, 7-12 January 2008.
  • Zagibalov T, Carroll J. “Automatic seed word selection for unsupervised sentiment classification of Chinese text”. 22nd International Conference on Computational Linguistics (COLING’08), Manchester, UK, 18-22 August 2008.
  • Qui L, Zhang W, Hu C, Zhao K. “SELC: a self-supervised model for sentiment classification”. 18th Association for Computing Machinery conference on Information and Knowledge Management (ACM-CIKM), Hong Kong, China, 2-6 November 2009.
  • Rothfels J, Tibsirani J. “Unsupervised Sentiment Classification of English Movie Reviews Using Automatic Selection of Positive and Negative Sentiment Items”. http://nlp.stanford.edu/courses/cs224n/2010/reports/ rothfels-jtibs.pdf (15.01.2015).

Makine öğrenmesi yöntemlerinin görüş madenciliğinde kullanılması üzerine bir literatür araştırması

Year 2016, Volume 22, Issue 2, 111 - 122, 01.05.2016

Abstract

Görüş madenciliği, görüş sahibinin tutum, davranış, duygu gibi öznel bilgilerinin çıkarılması için doğal dil işleme, metin madenciliği, hesaplamalı dilbilim gibi bilim alanlarının tekniklerini kullanan güncel bir araştırma alanıdır. Görüş madenciliği işleminin temel olarak bir sınıflandırma problemi olarak ele alınması mümkündür. Bu nedenle, makine öğrenmesine dayalı yöntemler sıklıkla görüş sınıflandırma amacıyla uygulanmaktadır. Görüş madenciliğinde makine öğrenmesine dayalı yöntemler temel olarak, öğreticili, yarı-öğreticili ve öğreticisiz yöntemler olmak üzere üç temel sınıf altında incelenmektedir. Bu çalışma kapsamında, görüş madenciliği alanında gerçekleştirilen temel makine öğrenmesine dayalı çalışmalar ve her bir makine öğrenmesi yönteminin güçlü ve zayıf yönleri ele alınmaktadır.

References

  • Ganesan K, Kim H. D. “Opinion Mining Tutorial (Sentiment Analysis)”.http://www.slideshare.net/KavitaGanesan/op inion-mining-kavitahyunduk00 (15.01.2015).
  • Medhat W, Hassan A, Korashy H. “Sentiment analysis algorithms and applications: a survey”. Ain Shams Engineering Journal, 5(4), 1093-1113, 2014
  • Taboada M, Brooke J, Tofiloski M, Voll K, Stede M. “Lexicon-Based methods for sentiment analysis”. Computational Linguistics, 37(2), 267-307, 2011.
  • Pang B, Lee L, Vaithyanathan S. “Thumbs up?: Sentiment classification using machine learning techniques”. Conference on Empirical Methods in Natural Language Processing (EMNLP), Philadelphia, PA, USA, 6-7 July 2002.
  • Pang B, Lee L. “A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts”. 42nd Annual Meeting of the Association for Computational Linguistics (ACL), Barcelona, Spain, 21-26 July 2004.
  • Whitelaw C, Garg N, Argamon S. “Using appraisal groups for sentiment analysis”. 14th ACM International Conference on Information and Knowledge Management (CIKM), Bremen, Germany, 31 October-5 November 2005.
  • Matsumoto S, Takamura H, Okumura M. “Sentiment classification using word sub-sequences and dependency sub-trees”. 9th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Hanoi, Vietnam, 18- 20 May 2005.
  • McDonald R, Hannan K, Neylon T, Wells M, Reynar J. “Structured models for fine-to-coarse sentiment analysis”. 45th Annual Meeting of the Association of Computational Linguistics, Prague, Czech Republic, 23-30 June 2007.
  • Zaidan OF, Eisner J, Piatko CD. “Using ‘annotator rationales’ to improve machine learning for text categorization”. Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT), Rochester, NY, USA, 22-27 April 2007.
  • Tan S, Zhang J. “An empirical study of sentiment analysis for Chinese document”. Expert Systems with Applications, 34(4), 2622-2629, 2008.
  • Prabowo R, Thelwall M. “Sentiment Analysis: a combined approach”. Journal of Informetrics, 3(2), 143-157, 2009.
  • Yassenalina A, Yue Y, Cardie C. “Multi-Level structured models for document-level sentiment classification”. Conference on Empirical Methods in Natural Language Processing (EMNLP), Boston, MA, USA, 9-11 October 2010.
  • Qui G, He X, Zhang F, Shi Y, Bu J, Chen C. “DASA: dissatisfaction-oriented advertising based on sentiment analysis”. Expert Systems with Application, 37(9), 6182-6191, 2010.
  • Zhao YY, Qin B, Liu T. “Integrating intra-and inter- document evidences for improving sentence sentiment classification”. 1417-1425, 2010. Sinica, 36(10),
  • Bai X. “Predicting consumer sentiments from online text”. Decision Support Systems, 50(4), 732-742, 2011.
  • Chen CC, Tseng YD. “Quality evaluation of product reviews using an information quality framework”. Decision Support Systems, 50(4), 755-768, 2011.
  • Wang S, Li D, Song X, We, Y, Li H. “A feature selection method based on improved fisher’s discriminant ration for text sentiment classification”. Expert Systems with Applications, 38(7), 8696-8702, 2011.
  • Xia R, Zong C, Li S. “Ensemble of feature sets and classification algorithms”. Information Sciences, 181(6), 1138-1152, 2011.
  • Kang H, Yoo SJ, Han M. “Senti-Lexicon and improved Naïve Bayes algorithms for sentiment analysis of restaurant reviews”. Expert Systems with Applications, 39(5), 6000- 6010, 2012.
  • Sun Y, Wong AKC, Kamel SM. “Classification of imbalanced data: a review”. International Journal of Pattern Recognition 687-719, 2009. Intelligence, 23(4),
  • Li YM, Li TY. “Deriving Market intelligence from microblogs”. Decision Support Systems, 55(1), 206-217, 2013.
  • Moraes R, Valiati JF, Neto WPG. “Document-Level sentiment classification: an empirical comparison between SVM and ANN”. Expert Systems with Applications, 40(2), 621-633, 2013.
  • Wang G, Sun J, Ma J, Xu K, Gu J. “Sentiment classification: the contribution of ensemble learning”. Decision Support Systems, 57, 77-93, 2014.
  • Chalothom T, Ellman J. Simple Approaches of Sentiment Analysis via Ensemble Learning. Editor: Kim KJ. Information Science and Applications, 631-639, Berlin, Germany, Springer, 2015.
  • Zheng L, Wang H, Gao S. “Sentimental feature selection for sentiment analysis of Chinese online reviews”. International Journal of Machine Learning and Cybernetics, 1-10, 2015.
  • Lin C. Probabilistic Topic Models for Sentiment Analysis on the Web. PhD Thesis, University of Exeter, Exeter, UK, 2011.
  • Aue A, Gamon M. “Customizing sentiment classifiers to new domains: a case study”. International Conference on Recent Advances in Natural Language Processing (RANLP), Borovets, Bulgaria, 21-23 September 2005.
  • Tan S, Wu G, Tang H, Cheng X. “A novel scheme for domain- transfer problem in the context of sentiment analysis”. Conference on Information and Knowledge Management (CIKM), Lisbon, Portugal, 6-10 November 2007.
  • Blitzer J, Dredze M, Pereira F. “Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification”. 45th Annual Meeting of the Association for Computational Linguistics (ACL), Prague, Czech Republic, 25-27 June 2007.
  • Mihalcea R, Banae C, Wiebe J. “Learning multilingual subjective language via cross-lingual projections”. 45th Annual Meeting of the Association for Computational Linguistics (ACL), Prague, Czech Republic, 25-27 June 2007.
  • Li S, Zong C. “Multi-Domain sentiment classification”. 46th Annual Meeting of the Association for Computational Linguistics (ACL), Columbus, OH, USA, 19-20 June 2008.
  • Banae C, Mihalcea R, Wiebe J. “Multilingual subjectivity analysis using machine translation”. Conference on Empirical Methods in Natural Language Processing (EMNLP), Honolulu, HI, USA, 25-27 October 2008.
  • Li T, Zhang Y, Sindhwani V. “A non-negative matrix tri- factorization approach to sentiment classification with lexical prior knowledge”. 47th Annual Meeting of the Association for Computational Linguistics (ACL), Suntec, Singapore, 2-7 August 2009.
  • Dasgupta S, Ng V. “Mine the easy, classify the hard: a semi- supervised classification”. 47th Annual Meeting of the Association for Computational Linguistics (ACL), Suntec, Singapore, 2-7 August 2009. to automatic sentiment
  • Wan X. “Co-training for cross-lingual sentiment classification”. 47th Annual Meeting of the Association for Computational Linguistics (ACL), Suntec, Singapore, 2-7 August 2009.
  • Li S, Huang CR, Zhou G, Lee SYM. “Employing personal/impersonal views in supervised and semi- supervised sentiment classification”. 48th Annual Meeting of the Association for Computational Linguistics (ACL), Uppsala, Sweden, 11-16 July 2010.
  • He Y, Zhou D. “Self-Training from labelled features for sentiment analysis”. Information Processing and Management, 47(4), 606-616, 2011.
  • He Y, Lin C, Alani H. “Automatically extracting polarity- bearing topics for cross-domain sentiment classification”. 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (ACL-HLT), Portland, OR, USA, 19-24 June 2011.
  • Hernandez OJ, Rodriguez JD, Alzate, L, Lucania M, Inza I, Lozano JA. “Approaching sentiment analysis by using semi-supervised classifiers”. Neurocomputing, 92, 98-115, 2012. of multi-dimensional
  • Hajmohammadi MS, Ibrahim R, Selamat A. “Bi-View semi-supervised active learning for cross-lingual sentiment classification”. Information Processing and Management, 50(5), 718-732, 2014.
  • Hajmohammadi MS, Ibrahim R, Selamat A. “Cross-Lingual sentiment classification using multiple source languages in multi-view semi-supervised learning”. Engineering Applications of Artificial Intelligence, 36, 195-203, 2014.
  • Hajmohammadi MS, Ibrahim R, Selamat A, Fujita H. “Combination of active learning and self-training for cross-lingual sentiment classification with density analysis of unlabelled samples”. Information Sciences, 317, 67-77, 2015.
  • Turney P, Littman M. “Unsupervised learning of semantic orientation from a hundred-billion-word corpus”. Institute for Information Technology, National Research Council, Ottawa, Ontario, Canada, Technical report, ERB- 1094, 2002.
  • Andreevskai A, Bergler S. “When specialists and generalists work together: overcoming domain dependence in sentiment tagging”. 46th Annual Meeting of the Association for Computational Linguistics (ACL), Columbus, OH, USA, 19-20 June 2008.
  • Zagibalov T, Carroll J. “Unsupervised classification of sentiment and objectivity in Chinese text”. International Joint Conference on Natural Language Processing (IJCNLP), Hyderabad, India, 7-12 January 2008.
  • Zagibalov T, Carroll J. “Automatic seed word selection for unsupervised sentiment classification of Chinese text”. 22nd International Conference on Computational Linguistics (COLING’08), Manchester, UK, 18-22 August 2008.
  • Qui L, Zhang W, Hu C, Zhao K. “SELC: a self-supervised model for sentiment classification”. 18th Association for Computing Machinery conference on Information and Knowledge Management (ACM-CIKM), Hong Kong, China, 2-6 November 2009.
  • Rothfels J, Tibsirani J. “Unsupervised Sentiment Classification of English Movie Reviews Using Automatic Selection of Positive and Negative Sentiment Items”. http://nlp.stanford.edu/courses/cs224n/2010/reports/ rothfels-jtibs.pdf (15.01.2015).

Details

Primary Language English
Journal Section Review Article
Authors

Aytuğ ONAN


Serdar KORUKOĞLU
0000-0002-4230-8447

Publication Date May 1, 2016
Published in Issue Year 2016, Volume 22, Issue 2

Cite

Bibtex @ { pajes219181, journal = {Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi}, issn = {1300-7009}, eissn = {2147-5881}, address = {}, publisher = {Pamukkale University}, year = {2016}, volume = {22}, number = {2}, pages = {111 - 122}, title = {A review of literature on the use of machine learning methods for opinion mining}, key = {cite}, author = {Onan, Aytuğ and Korukoğlu, Serdar} }
APA Onan, A. & Korukoğlu, S. (2016). A review of literature on the use of machine learning methods for opinion mining . Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi , 22 (2) , 111-122 . Retrieved from https://dergipark.org.tr/en/pub/pajes/issue/20566/219181
MLA Onan, A. , Korukoğlu, S. "A review of literature on the use of machine learning methods for opinion mining" . Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 22 (2016 ): 111-122 <https://dergipark.org.tr/en/pub/pajes/issue/20566/219181>
Chicago Onan, A. , Korukoğlu, S. "A review of literature on the use of machine learning methods for opinion mining". Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 22 (2016 ): 111-122
RIS TY - JOUR T1 - A review of literature on the use of machine learning methods for opinion mining AU - Aytuğ Onan , Serdar Korukoğlu Y1 - 2016 PY - 2016 N1 - DO - T2 - Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi JF - Journal JO - JOR SP - 111 EP - 122 VL - 22 IS - 2 SN - 1300-7009-2147-5881 M3 - UR - Y2 - 2022 ER -
EndNote %0 Pamukkale University Journal of Engineering Sciences A review of literature on the use of machine learning methods for opinion mining %A Aytuğ Onan , Serdar Korukoğlu %T A review of literature on the use of machine learning methods for opinion mining %D 2016 %J Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi %P 1300-7009-2147-5881 %V 22 %N 2 %R %U
ISNAD Onan, Aytuğ , Korukoğlu, Serdar . "A review of literature on the use of machine learning methods for opinion mining". Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 22 / 2 (May 2016): 111-122 .
AMA Onan A. , Korukoğlu S. A review of literature on the use of machine learning methods for opinion mining. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. 2016; 22(2): 111-122.
Vancouver Onan A. , Korukoğlu S. A review of literature on the use of machine learning methods for opinion mining. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. 2016; 22(2): 111-122.
IEEE A. Onan and S. Korukoğlu , "A review of literature on the use of machine learning methods for opinion mining", Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, vol. 22, no. 2, pp. 111-122, May. 2016

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