SINAI at SemEval-2017 Task 4: User based classification

Salud María Jiménez-Zafra, Arturo Montejo-Ráez, Maite Martin, L. Alfonso Ureña-López


Abstract
This document describes our participation in SemEval-2017 Task 4: Sentiment Analysis in Twitter. We have only reported results for subtask B - English, determining the polarity towards a topic on a two point scale (positive or negative sentiment). Our main contribution is the integration of user information in the classification process. A SVM model is trained with Word2Vec vectors from user’s tweets extracted from his timeline. The obtained results show that user-specific classifiers trained on tweets from user timeline can introduce noise as they are error prone because they are classified by an imperfect system. This encourages us to explore further integration of user information for author-based Sentiment Analysis.
Anthology ID:
S17-2104
Volume:
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
Month:
August
Year:
2017
Address:
Vancouver, Canada
Venue:
*SEMEVAL
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
634–639
Language:
URL:
https://www.aclweb.org/anthology/S17-2104
DOI:
10.18653/v1/S17-2104
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/S17-2104.pdf