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Lexicon information in neural sentiment analysis: a multi-task learning approach

Jeremy Barnes
Department of Informatics, University of Oslo, Norway

Samia Touileb
Department of Informatics, University of Oslo, Norway

Lilja Øvrelid
Department of Informatics, University of Oslo, Norway

Erik Velldal
Department of Informatics, University of Oslo, Norway

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Ingår i: Proceedings of the 22nd Nordic Conference on Computational Linguistics (NoDaLiDa), September 30 - October 2, Turku, Finland

Linköping Electronic Conference Proceedings 167:19, s. 175--186

NEALT Proceedings Series 42:19, s. 175--186

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Publicerad: 2019-10-02

ISBN: 978-91-7929-995-8

ISSN: 1650-3686 (tryckt), 1650-3740 (online)

Abstract

This paper explores the use of multi-task learning (MTL) for incorporating external knowledge in neural models. Specifically, we show how MTL can enable a BiLSTM sentiment classifier to incorporate information from sentiment lexicons. Our MTL set-up is shown to improve model performance (compared to a single-task set-up) on both English and Norwegian sentence-level sentiment datasets. The paper also introduces a new sentiment lexicon for Norwegian.

Nyckelord

sentiment sentiment analysis sentiment lexicons multi-task learning neural NLP

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