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Academic Paper


Title: Negation detection for sentiment analysis: A case study in Spanish
Author: Salud Jiménez-Zafra
Author: Noa Cruz-Díaz
Author: Maite Taboada
Author: María Martín-Valdivia
Linguistic Field: Computational Linguistics
Subject Language: Spanish
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.

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This article appears IN Natural Language Engineering Vol. 27, Issue 2, which you can READ on Cambridge's site .

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