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Authors: Mohammed Jabreel 1 and Antonio Moreno 2

Affiliations: 1 Universitat Rovira i Virgili and Hodeidah University, Spain ; 2 Universitat Rovira i Virgili, Spain

ISBN: 978-989-758-246-2

Keyword(s): Twitter, Target-dependent Sentiment Analysis, Recurrent Neural Networks.

Abstract: Targeted sentiment analysis classifies the sentiment polarity towards a certain target in a given text. In this paper, we propose a target-dependent bidirectional gated recurrent unit (TD-biGRU) for target-dependent sentiment analysis of tweets. The proposed model has the ability to represent the interaction between the targets and their contexts. We have evaluated the effectiveness of the proposed model on a benchmark dataset from Twitter. The experiments show that our proposed model outperforms the state-of-the-are methods for target-dependent sentiment analysis.

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Paper citation in several formats:
Jabreel, M. and Moreno, A. (2017). Target-dependent Sentiment Analysis of Tweets using a Bi-directional Gated Recurrent Unit.In Proceedings of the 13th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST, ISBN 978-989-758-246-2, pages 80-87. DOI: 10.5220/0006299900800087

@conference{webist17,
author={Mohammed Jabreel. and Antonio Moreno.},
title={Target-dependent Sentiment Analysis of Tweets using a Bi-directional Gated Recurrent Unit},
booktitle={Proceedings of the 13th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,},
year={2017},
pages={80-87},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006299900800087},
isbn={978-989-758-246-2},
}

TY - CONF

JO - Proceedings of the 13th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,
TI - Target-dependent Sentiment Analysis of Tweets using a Bi-directional Gated Recurrent Unit
SN - 978-989-758-246-2
AU - Jabreel, M.
AU - Moreno, A.
PY - 2017
SP - 80
EP - 87
DO - 10.5220/0006299900800087

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