Please use this identifier to cite or link to this item:
http://hdl.handle.net/10174/10342
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| Title: | senti.ue-en: an approach for informally written short texts in semeval-2013 sentiment analysis task |
| Authors: | Saias, jose
Fernandes, Hilário |
| Keywords: | opinion mining
sentiment analysis
NLP
Machine Learning |
| Issue Date: | Jun-2013 |
| Publisher: | Association for Computational Linguistics |
| Citation: | José Saias and Hilário Fernandes.
senti.ue-en: an approach for informally written short texts in semeval-2013 sentiment analysis task.
In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013), pages 508-512, Atlanta, Georgia, USA, June 2013. Association for Computational Linguistics |
| Abstract: | This article describes a Sentiment Analysis (SA) system named senti.ue-en,
built for participation in SemEval-2013 Task 2, a Twitter SA challenge.
In both challenge subtasks we used the same supervised machine learning approach,
including two classifiers in pipeline, with 22 semantic oriented features, such as polarized term presence and index, and negation presence.
Our system achieved a better score on Task A (0.7413) than in the Task B (0.4785).
In the first subtask, there is a better result for SMS than the obtained for the more trained type of data, the tweets. |
| URI: | http://aclweb.org/anthology//S/S13/S13-2084.pdf
http://hdl.handle.net/10174/10342 |
| Type: | article |
| Appears in Collections: | INF - Artigos em Livros de Actas/Proceedings
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