Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
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
WSA '10 Proceedings of the NAACL HLT 2010 Workshop on Computational Linguistics in a World of Social Media
Sentiment analysis of Twitter data
LSM '11 Proceedings of the Workshop on Languages in Social Media
Survey on mining subjective data on the web
Data Mining and Knowledge Discovery
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Law
TOM: Twitter opinion mining framework using hybrid classification scheme
Decision Support Systems
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This paper presents a novel approach in Sentiment Polarity Detection on Twitter posts, by extracting a vector of weighted nodes from the graph of WordNet. These weights are used on SentiWordNet to compute a final estimation of the polarity. Therefore, the method proposes a non-supervised solution that is domain-independent. The evaluation over a generated corpus of tweets shows that this technique is promising.