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Title: Senti.ue: Tweet Overall Sentiment Classification Approach for SemEval-2014 Task 9
Authors: Saias, José
Keywords: NLP
Artificial Intelligence
Machine Leaning
Sentiment Analysis
Issue Date: Aug-2014
Publisher: Association for Computational Linguistics
Citation: J. Saias, “Senti.ue: Tweet overall sentiment classification approach for semeval-2014 task 9,” in Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), (Dublin, Ireland), pp. 546–550, Association for Computational Linguistics and Dublin City University, August 2014. ISBN 978-1-941643-24-2.
Abstract: This document describes the senti.ue system and how it was used for partici- pation in SemEval-2014 Task 9 challenge. Our system is an evolution of our prior work, also used in last year’s edition of Sentiment Analysis in Twitter. This sys- tem maintains a supervised machine learn- ing approach to classify the tweet overall sentiment, but with a change in the used features and the algorithm. We use a re- stricted set of 47 features in subtask B and 31 features in subtask A. In the constrained mode, and for the five data sources, senti.ue achieved a score between 78,72 and 84,05 in subtask A, and a score between 55,31 and 71,39 in sub- task B. For the unconstrained mode, our score was slightly below, except for one case in subtask A.
Type: article
Appears in Collections:INF - Artigos em Livros de Actas/Proceedings

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