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Multitask Learning for Fine-Grained Twitter Sentiment Analysis

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. Green open access

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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
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 > 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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