Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Thumbs up?: sentiment classification using machine learning techniques
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Personalizing PageRank for word sense disambiguation
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Using emoticons to reduce dependency in machine learning techniques for sentiment classification
ACLstudent '05 Proceedings of the ACL Student Research Workshop
Random walks for text semantic similarity
TextGraphs-4 Proceedings of the 2009 Workshop on Graph-based Methods for Natural Language Processing
Identifying text polarity using random walks
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
WSA '10 Proceedings of the NAACL HLT 2010 Workshop on Computational Linguistics in a World of Social Media
Robust sentiment detection on Twitter from biased and noisy data
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Enhanced sentiment learning using Twitter hashtags and smileys
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Target-dependent Twitter sentiment classification
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Sentiment analysis of Twitter data
LSM '11 Proceedings of the Workshop on Languages in Social Media
Sentiment-Preserving reduction for social media analysis
CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
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This paper presents a novel approach to Sentiment Polarity Classification in Twitter posts, by extracting a vector of weighted nodes from the graph of WordNet. These weights are used in SentiWordNet to compute a final estimation of the polarity. Therefore, the method proposes a non-supervised solution that is domain-independent. The evaluation of a generated corpus of tweets shows that this technique is promising.