Balikas, G;
Moura, S;
Amini, M-R;
(2017)
Multitask Learning for Fine-Grained Twitter Sentiment Analysis.
In: Kando, Noriko and Sakai, Tetsuya and Joho, Hideo, (eds.)
SIGIR '17 Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval.
ACM (Association for Computing Machinery): New York, USA.
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Abstract
Traditional sentiment analysis approaches tackle problems like ternary (3-category) and fine-grained (5-category) classification by learning the tasks separately. We argue that such classification tasks are correlated and we propose a multitask approach based on a recurrent neural network that benefits by jointly learning them. Our study demonstrates the potential of multitask models on this type of problems and improves the state-of-the-art results in the fine-grained sentiment classification problem.
Type: | Proceedings paper |
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Title: | Multitask Learning for Fine-Grained Twitter Sentiment Analysis |
Event: | 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '17), 7-11 August 2017, Tokyo, Japan |
Location: | Tokyo |
ISBN-13: | 978-1-4503-5022-8 |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1145/3077136.3080702 |
Language: | English |
Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
Keywords: | Text Mining; Sentiment Analysis; Deep Learning; Multitask Learning, Twitter Analysis; bidirectional LSTM; Text classification |
UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
URI: | https://discovery.ucl.ac.uk/id/eprint/10075204 |
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