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Negation detection for sentiment analysis: A case study in Spanish

Published online by Cambridge University Press:  07 July 2020

Salud María Jiménez-Zafra Open the ORCID record for Salud María Jiménez-Zafra [Opens in a new window] ,
Noa P. Cruz-Díaz ,
Maite Taboada Open the ORCID record for Maite Taboada [Opens in a new window]  and
María Teresa Martín-Valdivia
Salud María Jiménez-Zafra*
Affiliation:
SINAI, Centro de Estudios Avanzados en TIC (CEATIC), Universidad de Jaén, Spain
Noa P. Cruz-Díaz
Affiliation:
Centro de Excelencia de Inteligencia Artificial, Bankia, Madrid, Spain
Maite Taboada
Affiliation:
Discourse Processing Lab, Simon Fraser University, Burnaby, BC, Canada
María Teresa Martín-Valdivia
Affiliation:
SINAI, Centro de Estudios Avanzados en TIC (CEATIC), Universidad de Jaén, Spain
*
*Corresponding author. E-mail: sjzafra@ujaen.es
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Abstract

Accurate negation identification is one of the most important tasks in the context of sentiment analysis. In order to correctly interpret the sentiment value of a particular expression, we need to identify whether it is in the scope of negation. While much of the work on negation detection has focused on English, we have seen recent developments that provide accurate identification of negation in other languages. In this paper, we provide an overview of negation detection systems and describe an implementation of a Spanish system for negation cue detection and scope identification. We apply this system to the sentiment analysis task, confirming also for Spanish that improvements can be gained from accurate negation detection. The paper contributes an implementation of negation detection for sentiment analysis in Spanish and a detailed error analysis. This is the first work in Spanish in which a machine learning negation processing system is applied to the sentiment analysis task. Existing methods have used negation rules that have not been assessed, perhaps because the first Spanish corpus annotated with negation for sentiment analysis has only recently become available.

Keywords

Negation processingSentiment analysisMachine learningNatural Language Processing
Type
Article
Information
Natural Language Engineering , Volume 27 , Issue 2 , March 2021 , pp. 225 - 248
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Copyright
© The Author(s), 2020. Published by Cambridge University Press

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