A Knowledge-Based Model for Polarity Shifters

  1. Blázquez-López, Yolanda 1
  1. 1 Universidad Nacional de Educación a Distancia
    info

    Universidad Nacional de Educación a Distancia

    Madrid, España

    ROR https://ror.org/02msb5n36

Revista:
Journal of Computer-Assisted Linguistic Research

ISSN: 2530-9455

Año de publicación: 2022

Número: 6

Páginas: 87-107

Tipo: Artículo

DOI: 10.4995/JCLR.2022.18807 DIALNET GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: Journal of Computer-Assisted Linguistic Research

Objetivos de desarrollo sostenible

Resumen

Polarity shifting can be considered one of the most challenging problems in the context of Sentiment Analysis. Polarity shifters, also known as contextual valence shifters (Polanyi and Zaenen 2004), are treated as linguistic contextual items that can increase, reduce or neutralise the prior polarity of a word called focus included in an opinion. The automatic detection of such items enhances the performance and accuracy of computational systems for opinion mining, but this challenge remains open, mainly for languages other than English. From a symbolic approach, we aim to advance in the automatic processing of the polarity shifters that affect the opinions expressed on tweets, both in English and Spanish. To this end, we describe a novel knowledge-based model to deal with three dimensions of contextual shifters: negation, quantification, and modality (or irrealis).

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