UMCC DLSI: Sentiment Analysis in Twitter using Polirity Lexicons and Tweet Similarity

Abstract

This paper describes a system submitted 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 classifiers, K-NN using the lexical Levenshtein metric and a Dagging model trained over attributes extracted from annotated corpora and sentiment lexicons. Phrases that fit the distance thresholds were automatically 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.