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Enhancing Spanish Aspect-Based Sentiment Analysis Through Deep Learning Approach

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Progress in Artificial Intelligence and Pattern Recognition (IWAIPR 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14335))

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Abstract

Aspect-based sentiment analysis is the task of monitoring user sentiment on textual opinions about the characteristics of a given entity. Recognizing the aspects present in the opinion and determining its sentimental orientation (positive or negative) in a similar way as if it were done by a human being continues to be a challenging task, but at the same time necessary. Achieving quality improvement of existing aspect-based sentiment analysis solutions remains a challenge and the vast majority of solutions reported in this task are focused on the English language, so further progress is needed in languages like Spanish. This paper presents an aspect-based sentiment analysis method which uses language models based on Transformers (BERT models) with a linear layer to extract aspects from opinions in Spanish and a similar model to perform Aspect Sentiment Classification, demonstrating that its use allows us to surpass the state of the art for said language. The proposed solution was evaluated using the Semeval2016 Task 5 dataset achieving promising results, respect those reported by other solutions.

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Acknowledgments

This work has been partially supported by FEDER and the State Research Agency (AEI) of the Spanish Ministry of Economy and Competition under grant SAFER: PID2019-104735RB-C42 (AEI/FEDER, UE).

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Authors and Affiliations

  1. Universidad Tecnológica de La Habana “José Antonio Echeverría”, Havana, La Habana, Cuba

    Patricia Montañez Castelo & Alfredo Simón-Cuevas

  2. Universidad de Castilla-La Mancha, Ciudad Real, Spain

    José A. Olivas & Francisco P. Romero

Authors
  1. Patricia Montañez Castelo
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  2. Alfredo Simón-Cuevas
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  3. José A. Olivas
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  4. Francisco P. Romero
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Corresponding author

Correspondence to Alfredo Simón-Cuevas .

Editor information

Editors and Affiliations

  1. Universidad de las Ciencias Informáticas, Havana, Cuba

    Yanio Hernández Heredia

  2. Universidad de las Ciencias Informáticas, Havana, Cuba

    Vladimir Milián Núñez

  3. Universidad de las Ciencias Informáticas, Havana, Cuba

    José Ruiz Shulcloper

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Montañez Castelo, P., Simón-Cuevas, A., Olivas, J.A., Romero, F.P. (2024). Enhancing Spanish Aspect-Based Sentiment Analysis Through Deep Learning Approach. In: Hernández Heredia, Y., Milián Núñez, V., Ruiz Shulcloper, J. (eds) Progress in Artificial Intelligence and Pattern Recognition. IWAIPR 2023. Lecture Notes in Computer Science, vol 14335. Springer, Cham. https://doi.org/10.1007/978-3-031-49552-6_19

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  • DOI: https://doi.org/10.1007/978-3-031-49552-6_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-49551-9

  • Online ISBN: 978-3-031-49552-6

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