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Improving sentiment analysis with multi-task learning of negation

Published online by Cambridge University Press:  11 November 2020

Jeremy Barnes Open the ORCID record for Jeremy Barnes [Opens in a new window] ,
Erik Velldal  and
Lilja Øvrelid
Jeremy Barnes*
Affiliation:
Language Technology Group, University of Oslo, Oslo, Norway
Erik Velldal
Affiliation:
Language Technology Group, University of Oslo, Oslo, Norway
Lilja Øvrelid
Affiliation:
Language Technology Group, University of Oslo, Oslo, Norway
*
*Corresponding author. E-mail: [email protected]
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Abstract

Sentiment analysis is directly affected by compositional phenomena in language that act on the prior polarity of the words and phrases found in the text. Negation is the most prevalent of these phenomena, and in order to correctly predict sentiment, a classifier must be able to identify negation and disentangle the effect that its scope has on the final polarity of a text. This paper proposes a multi-task approach to explicitly incorporate information about negation in sentiment analysis, which we show outperforms learning negation implicitly in an end-to-end manner. We describe our approach, a cascading and hierarchical neural architecture with selective sharing of Long Short-term Memory layers, and show that explicitly training the model with negation as an auxiliary task helps improve the main task of sentiment analysis. The effect is demonstrated across several different standard English-language data sets for both tasks, and we analyze several aspects of our system related to its performance, varying types and amounts of input data and different multi-task setups.

Keywords

sentiment analysisnegation detectionmulti-task
Type
Article
Information
Natural Language Engineering , Volume 27 , Issue 2 , March 2021 , pp. 249 - 269
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Copyright
© The Author(s), 2020. Published by Cambridge University Press

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