Deep Learning Approach for Aspect-Based Sentiment Analysis of Restaurants Reviews in Spanish

Authors

  • Bella Citlali Martínez-Seis Instituto Politécnico Nacional
  • Obdulia Pichardo-Lagunas Instituto Politécnico Nacional
  • Sabino Miranda NFOTEC Centro de Investigación e Innovación en Tecnologías de la Información y Comunicación
  • Israel Josafat Perez-Cazares Instituto Politécnico Nacional
  • Jorge Armando Rodriguez-Gonzalez Instituto Politécnico Nacional

DOI:

https://doi.org/10.13053/cys-26-2-4258

Keywords:

Customer reviews, polarity classification, sentiment analysis

Abstract

Online reviews of products and services have become important for customers and enterprises. Recent research focuses on analyzing and managing those kinds of reviews using natural language processing. This paper focuses on aspect-based sentiment analysis for reviews in Spanish. First, the reviews data sets are normalized into different inputs of the neural networks. Then, our approach combines two deep learning models architectures to determine a positive or negative assessment and identify the most important characteristics or aspects of the text. We develop two architectures for aspect detection and three architectures for sentiment analysis. Merging the deep learning models, we tested our approach in restaurant reviews and compared them with state-of-the-art methods.

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Published

2022-06-15

Issue

Vol. 26 No. 2 (2022): Emerging Issues and Applications of Fuzzy Systems, Neural Networks and Metaheuristics (Castillo, Melin, Montiel, Valdez)

Section

Articles

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