[1] Ganesan K, Kim H. D. “Opinion Mining Tutorial (Sentiment Analysis)”.http://www.slideshare.net/KavitaGanesan/opinion-mining-kavitahyunduk00 (15.01.2015).
[2] Medhat W, Hassan A, Korashy H. “Sentiment analysis algorithms and applications: a survey”. Ain Shams Engineering Journal, 5(4), 1093-1113, 2014
[3] Taboada M, Brooke J, Tofiloski M, Voll K, Stede M. “Lexicon-Based methods for sentiment analysis”. Computational Linguistics, 37(2), 267-307, 2011.
[4] 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.
[5] 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.
[6] 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.
[7] 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.
[8] 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.
[9] 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.
Pamukkale Univ Muh Bilim Derg, 22(2), 111-122, 2016
A. Onan, S. Korukoğlu
121
[10] Tan S, Zhang J. “An empirical study of sentiment analysis for Chinese document”. Expert Systems with Applications, 34(4), 2622-2629, 2008.
[11] Prabowo R, Thelwall M. “Sentiment Analysis: a combined approach”. Journal of Informetrics, 3(2), 143-157, 2009.
[12] 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.
[13] 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.
[14] Zhao YY, Qin B, Liu T. “Integrating intra-and inter-document evidences for improving sentence sentiment classification”. Acta Automatica Sinica, 36(10), 1417-1425, 2010.
[15] Bai X. “Predicting consumer sentiments from online text”. Decision Support Systems, 50(4), 732-742, 2011.
[16] Chen CC, Tseng YD. “Quality evaluation of product reviews using an information quality framework”. Decision Support Systems, 50(4), 755-768, 2011.
[17] 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.
[18] Xia R, Zong C, Li S. “Ensemble of feature sets and classification algorithms”. Information Sciences, 181(6), 1138-1152, 2011.
[19] 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.
[20] Sun Y, Wong AKC, Kamel SM. “Classification of imbalanced data: a review”. International Journal of Pattern Recognition and Artificial Intelligence, 23(4), 687-719, 2009.
[21] Li YM, Li TY. “Deriving Market intelligence from microblogs”. Decision Support Systems, 55(1), 206-217, 2013.
[22] 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.
[23] Wang G, Sun J, Ma J, Xu K, Gu J. “Sentiment classification: the contribution of ensemble learning”. Decision Support Systems, 57, 77-93, 2014.
[24] 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.
[25] 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.
[26] Lin C. Probabilistic Topic Models for Sentiment Analysis on the Web. PhD Thesis, University of Exeter, Exeter, UK, 2011.
[27] 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.
[28] 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.
[29] 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.
[30] 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.
[31] 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.
[32] 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.
[33] 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.
[34] Dasgupta S, Ng V. “Mine the easy, classify the hard: a semi-supervised approach to automatic sentiment classification”. 47th Annual Meeting of the Association for Computational Linguistics (ACL), Suntec, Singapore, 2-7 August 2009.
[35] 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.
[36] 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.
[37] He Y, Zhou D. “Self-Training from labelled features for sentiment analysis”. Information Processing and Management, 47(4), 606-616, 2011.
[38] 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.
[39] Hernandez OJ, Rodriguez JD, Alzate, L, Lucania M, Inza I, Lozano JA. “Approaching sentiment analysis by using semi-supervised learning of multi-dimensional classifiers”. Neurocomputing, 92, 98-115, 2012.
[40] 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.
[41] 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.
Pamukkale Univ Muh Bilim Derg, 22(2), 111-122, 2016
A. Onan, S. Korukoğlu
122
[42] 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.
[43] 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.
[44] 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.
[45] 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.
[46] 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.
[47] 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.
[48] 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).
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