R. Agerri and A. Garca-serrano, Qwordnet: Extracting polarity from wordnet senses, Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10), 2010.

S. Baccianella, A. Esuli, and F. Sebastiani, Sentiwordnet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining, Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10), 2010.

A. Balahur and M. Turchi, Multilingual sentiment analysis using machine translation?, Proceedings of the 3rd Workshop in Computational Approaches to Subjectivity and Sentiment Analysis, WASSA '12, pp.52-60, 2012.

A. Bermingham and A. F. Smeaton, A study of inter-annotator agreement for opinion retrieval, Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval, SIGIR '09, pp.784-785, 2009.
DOI : 10.1145/1571941.1572127

Y. Choi and C. Cardie, Learning with compositional semantics as structural inference for subsentential sentiment analysis, Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP '08, pp.793-801, 2008.
DOI : 10.3115/1613715.1613816

M. De-marneffe and C. D. Manning, Stanford typed dependencies manual, 2008.

G. Demiroz, B. Yanikoglu, D. Tapucu, and Y. Saygin, Learning Domain-Specific Polarity Lexicons, 2012 IEEE 12th International Conference on Data Mining Workshops, pp.674-679, 2012.
DOI : 10.1109/ICDMW.2012.120

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.423.3953

M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann et al., The WEKA data mining software, ACM SIGKDD Explorations Newsletter, vol.11, issue.1, pp.10-18, 2009.
DOI : 10.1145/1656274.1656278

M. Hu and B. Liu, Mining and summarizing customer reviews, Proceedings of the 2004 ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '04, pp.168-177, 2004.
DOI : 10.1145/1014052.1014073

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.76.2378

B. Liu, Sentiment Analysis and Opinion Mining, 2012.
DOI : 10.2200/s00416ed1v01y201204hlt016

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.244.9480

K. Moilanen and S. Pulman, Sentiment composition, Proceedings of Recent Advances in Natural Language Processing, pp.378-382, 2007.

P. Nakov, S. Rosenthal, Z. Kozareva, V. Stoyanov, A. Ritter et al., SemEval-2013 task 2: sentiment analysis in twitter, Proceedings of the 7th International Workshop on Semantic Evaluation, 2013.

B. Pang, L. Lee, and S. Vaithyanathan, Thumbs up? Sentiment classication using machine learning techniques, Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing, pp.79-86, 2002.

C. John and . Platt, Fast training of support vector machines using sequential minimal optimization, Advances in Kernel Methods, pp.185-208, 1999.

L. Polanyi and A. Zaenen, Contextual Valence Shifters, Computing Attitude and Affect in Text: Theory and Applications, pp.1-10, 2006.
DOI : 10.1007/1-4020-4102-0_1

S. Rosenthal, P. Nakov, A. Ritter, and V. Stoyanov, SemEval-2014 Task 9: Sentiment Analysis in Twitter, Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), 2014.
DOI : 10.3115/v1/S14-2009

P. Turney, Thumbs up or thumbs down? semantic orientation applied to unsupervised classication of reviews, Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp.417-424, 2002.

T. Wilson, J. Wiebe, and P. Hoff-mann, Recognizing contextual polarity in phrase-level sentiment analysis, Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing , HLT '05, 2005.
DOI : 10.3115/1220575.1220619