Please use this identifier to cite or link to this item:
http://hdl.handle.net/10174/13868
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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. |
| URI: | http://www.aclweb.org/anthology/S/S14/S14-2095.pdf
http://hdl.handle.net/10174/13868 |
| Type: | article |
| Appears in Collections: | INF - Artigos em Livros de Actas/Proceedings
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