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Gradiant-Analytics: Training Polarity Shifters with CRFs for Message Level Polarity Detection
2015
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)
In this paper we present our solution for obtaining sentiment at message-level of short sentences. The system combines the use of polarity dictionaries and Conditional Random Fields to obtain syntactic and semantic features, which are afterwards fed to a statistical classifier in order to obtain the sentence polarity. To improve results, an ensemble of classifiers was employed by combining the individual outputs with majority voting strategy. Our solution was evaluated in the SemEval 2015 Task
doi:10.18653/v1/s15-2090
dblp:conf/semeval/Cerezo-CostasC15
fatcat:3atyettb3fh3pjonflu5xmrzqm