@inproceedings{jimenez-zafra-etal-2017-sinai,
title = "{SINAI} at {S}em{E}val-2017 Task 4: User based classification",
author = "Jim{\'e}nez-Zafra, Salud Mar{\'\i}a and
Montejo-R{\'a}ez, Arturo and
Martin, Maite and
Ure{\~n}a-L{\'o}pez, L. Alfonso",
editor = "Bethard, Steven and
Carpuat, Marine and
Apidianaki, Marianna and
Mohammad, Saif M. and
Cer, Daniel and
Jurgens, David",
booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
month = aug,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S17-2104",
doi = "10.18653/v1/S17-2104",
pages = "634--639",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T SINAI at SemEval-2017 Task 4: User based classification
%A Jiménez-Zafra, Salud María
%A Montejo-Ráez, Arturo
%A Martin, Maite
%A Ureña-López, L. Alfonso
%Y Bethard, Steven
%Y Carpuat, Marine
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%Y Cer, Daniel
%Y Jurgens, David
%S Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F jimenez-zafra-etal-2017-sinai
%X 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.
%R 10.18653/v1/S17-2104
%U https://aclanthology.org/S17-2104
%U https://doi.org/10.18653/v1/S17-2104
%P 634-639
Markdown (Informal)
[SINAI at SemEval-2017 Task 4: User based classification](https://aclanthology.org/S17-2104) (Jiménez-Zafra et al., SemEval 2017)
ACL
- Salud María Jiménez-Zafra, Arturo Montejo-Ráez, Maite Martin, and L. Alfonso Ureña-López. 2017. SINAI at SemEval-2017 Task 4: User based classification. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 634–639, Vancouver, Canada. Association for Computational Linguistics.