Sentiment analysis in social media texts, 4th workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pp.120-128, 2013. ,
Modelling irony in twitter, EACL, pp.56-64, 2014. ,
The wacky wide web : a collection of very large linguistically processed web-crawled corpora. Language resources and evaluation, pp.209-226, 2009. ,
Harvesting opinions and emotions from social media textual resources, IEEE Internet Computing, pp.46-50, 2015. ,
Xgboost : A scalable tree boosting system, Proceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.785-794, 2016. ,
Links between perceptrons, mlps and svms, Proceedings of the Twenty-first International Conference on Machine Learning, p.23, 2004. ,
A review of features for the discrimination of twitter users : Application to the prediction of offline influence, Social Network Analysis and Mining, vol.6, issue.1, pp.1-23, 2016. ,
Leveraging large amounts of weakly supervised data for multi-language sentiment classification, International World Wide Web Conference Committee (IW3C2), 2017. ,
Sentiment analysis using support vector machine, International Journal of Innovative Research in Computer and Communication Engineering, 2014. ,
Extremely randomized trees, Mach. Learn, vol.63, issue.1, pp.3-42, 2006. ,
Twitter sentiment classification using distant supervision, CS224N Project Report, issue.112, 2009. ,
Identifying sarcasm in twitter : a closer look, Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics : Human Language Technologies : Short Papers, pp.581-586, 2011. ,
Kolmogorov complexity and information theory. with an interpretation in terms of questions and answers, Journal of Logic, Language and Information, vol.12, issue.4, pp.497-529, 2003. ,
Boosting and Additive Trees, 2001. ,
Vader : A parsimonious rule-based model for sentiment analysis of social media text, 2014. ,
Boosting bonsai trees for efficient features combination : application to speaker role identification, 2014. ,
The perfect solution for detecting sarcasm in tweets# not, 2013. ,
On the difficulty of automatically detecting irony : beyond a simple case of negation, Knowledge and Information Systems, vol.40, issue.3, pp.595-614, 2014. ,
Sarcasm as contrast between a positive sentiment and negative situation, EMNLP, pp.704-714, 2013. ,
Talep @ deft'15 : Le plus cooool des systèmes d'analyse de sentiment. DEFT-2015, pp.97-103, 2015. ,
Feature-rich part-of-speech tagging with a cyclic dependency network, Proceedings of the 2003 NAACL, pp.3-173, 2003. ,
Classification of sentimental reviews using machine learning techniques, 3rd International Conference on Recent Trends in Computing (ICRTC- 2015), pp.821-829, 2015. ,
Icwsm-a great catchy name : Semi-supervised recognition of sarcastic sentences in online product reviews, ICWSM, pp.162-169, 2010. ,
Just how mad are you ? finding strong and weak opinion clauses, Proceedings of the Nineteenth National Conference on Artificial Intelligence, Sixteenth Conference on Innovative Applications of Artificial Intelligence, pp.761-769, 2004. ,
Sentiment analysis using support vector machine, International Conference on Computer, Communications, and Control Technology (I4CT), 2014. ,
A new anew : evaluation of a word list for sentiment analysis in microblogs, Proceedings of the ESWC2011 Workshop on Making Sense of Microposts : Big things come in small packages CEUR Workshop Proceedings, pp.93-98, 2011. ,