Paper: UMCC_DLSI: Sentiment Analysis in Twitter using Polirity Lexicons and Tweet Similarity

ACL ID S14-2130
Title UMCC_DLSI: Sentiment Analysis in Twitter using Polirity Lexicons and Tweet Similarity
Venue Joint Conference on Lexical and Computational Semantics
Session
Year 2014
Authors

This paper describes a system sub- mitted to SemEval-2014 Task 4B: Sentiment Analysis in Twitter, by the team UMCC DLSI Sem integrated by researchers of the University of Matanzas, Cuba and the University of Alicante, Spain. The system adopts a cascade classification process that uses two classi- fiers, K-NN using the lexical Levenshtein metric and a Dagging model trained over attributes extracted from annotated cor- pora and sentiment lexicons. Phrases that fit the distance thresholds were automat- ically classified by the KNN model, the others, were evaluated with the Dagging model. This system achieved over 52.4% of correctly classified instances in the Twitter message-level subtask.