Pang, B, Lee, L, Vaithyanathan, S. Thumbs up? Sentiment classification using machine learning techniques. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2002, 79–86. Philadelphia, PA, USA: ACL.
Pang, B, Lee, L. A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts. In: Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics (ACL), 2004, 271–278. Barcelona, Spain: ACL. doi: 10.3115/1218955.1218990.
Taboada, M, Brooke, J, Tofiloski, M, Voll, K, Stede, M. Lexicon‐based methods for sentiment analysis. Comput Linguist 2011, 37:267–307. doi:10.1162/COLI_a_00049.
Ding, X, Liu, B, Yu, PS. A holistic lexicon‐based approach to opinion mining. In: Proceedings of the International Conference on Web Search and Data Mining (WSDM), 2008, 231–240. Palo Alto, CA, USA: ACM. doi: 10.1145/1341531.1341561.
Liu, K‐L., Li, W‐J, Guo, M. Emoticon smoothed language models for twitter sentiment analysis. In: Proceedings of the 26th Conference Association for the Advancement of Artificial Intelligence (AAAI) and 24th Innovative Applications of Artificial Intelligence (IAAI), Toronto, ON, Canada, 2012, 1678–1684.
Hu, Y, Lu, R, Chen, Y, Duan, J. Using a generative model for sentiment analysis. Int. J. Comput. Linguist. Chin. Lang. Process. 2007, 12:107–126.
Dong, L, Wei, F, Zhou, M, Xu, K. Adaptive multi‐compositionality for recursive neural nodels with applications to sentiment analysis. In: Proceedings of the 28th Conference of Association for the Advancement of Artificial Intelligence (AAAI), Québec, Canada, 2014, 1537–1543.
Dhande, L, Patnaik, G. Analyzing sentiment of movie review data using Naive Bayes neural classifier. Int J Emerg Trends Technol Comput Sci 2014, 3:313–320.
Santos, C, Gatti, M. Deep convolutional neural networks for sentiment analysis of short texts. In: Proceedings of the 25th International Conference on Computational Linguistics (COLING), Dublin, Ireland, 2014, 23–29.
Socher, R, Perelygin, A, Wu, JY, Chuang, J, Manning, CD, Ng, AY, Potts, C. Recursive deep models for semantic compositionality over a sentiment treebank. In: Proceedings Conference on Empirical Methods in Natural Language Processing (EMNLP), Seattle, WA, 2013, 1631–1642.
Barbieri, F, Saggion, H. Modelling irony in Twitter. In: Proceedings of the Student Research Workshop at 14th Conference of the European Chapter of the Association for Computational Linguistics (EACL), Gothenburg, Sweden, 2014, 56–64.
Liebrecht, C, Kunneman, F, van den Bosch, A. The perfect solution for detecting sarcasm in tweets #not. In: Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, Association for Computational Linguistics, Atlanta, GA, 2013, 29–37.
Reyes, A, Rosso, P. On the difficulty of automatically detecting irony: Beyond a simple case of negation. Knowl Inf Syst 2014, 40:595–614.
Liu, B. Sentiment Analysis and Opinion Mining. New York: Morgan %26 Claypool Publishers; 2012.
Manning, C, Raghavan, P, Schütze, H. An Introduction to Information Retrieval. Cambridge: Cambridge University Press; 2009.
Griffiths, T, Steyvers, M. Finding scientific topics. Proc Natl Acad Sci USA 2004, 101:5228–5235.
Fan, W, Bouguila, N. Online learning of a Dirichlet process mixture of generalized Dirichlet distributions for simultaneous clustering and localized feature selection. In: Proceedings of the 4th Asian Conference on Machine Learning (ACML), Singapore, 2012, 113–128.
Nigam, K, Lafferty, J, McCallum, A. Using maximum entropy for text classification. In: Proceedings of the IJCAI 99 Workshop on Machine Learning for Information Filtering, Stockholm, Sweden, 1999, 61–67.
Genkin, A, Lewis, D, Madigan, D. Large‐scale Bayesian logistic regression for text classification. Am Stat Assoc 2007, 49:291–304. doi:10.1198/004017007000000245.
Long, S, Freese, J. Regression Models for Categorical Dependent Variables using Stata. Stata Press; 2014.
Sundermeyer, M, Schlüter, R, Ney, H. LSTM neural networks for language modeling. In: Proceedings of Interspeech, Portland, OR, 2012.
Weninger, F, Bergmann, J, Schuller, B. Introducing CURRENNT—the Munich open‐source CUDA RecurREnt Neural Network Toolkit. J Mach Learn Res 2015, 16:547–551.