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Volume 14
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10.3390/app14031033
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Article

Senti-Sequence: Learning to Represent Texts for Sentiment Polarity Classification

by
Andres Ramos Magna
1,
Juan Zamora
2,* and
Hector Allende-Cid
3,4,5
1
Departamento de Tecnologías de Información, Universidad de Valparaíso, Calle Prat 856, Valparaíso 2361864, Chile
2
Instituto de Estadística, Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2830, Valparaíso 2340025, Chile
3
Escuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2241, Valparaíso 2362807, Chile
4
Knowledge Discovery, Fraunhofer-Institute of Intelligent Analysis and Information Systems (IAIS), Schloss Birlinghoven, 1, 53757 Sankt Augustin, Germany
5
Lamarr Institute for Machine Learning and Artificial Intelligence, 44227 Dortmund, Germany
*
Author to whom correspondence should be addressed.
Appl. Sci. 2024, 14(3), 1033; https://doi.org/10.3390/app14031033
Submission received: 6 December 2023 / Revised: 6 January 2024 / Accepted: 16 January 2024 / Published: 25 January 2024
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Abstract

The sentiment analysis task seeks to categorize opinionated documents as having overall positive or negative opinions. This task is very important to understand unstructured text content generated by users in different domains, such as online and entertainment platforms and social networks. In this paper, we propose a novel method for predicting the overall polarity in texts. First, a new polarity-aware vector representation is automatically built for each document. Then, a bidirectional recurrent neural architecture is designed to identify the emerging polarity. The attained results outperform all of the algorithms found in the literature in the binary polarity classification task.
Keywords: sentiment analysis; machine learning; deep learning sentiment analysis; machine learning; deep learning

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MDPI and ACS Style

Ramos Magna, A.; Zamora, J.; Allende-Cid, H. Senti-Sequence: Learning to Represent Texts for Sentiment Polarity Classification. Appl. Sci. 2024, 14, 1033. https://doi.org/10.3390/app14031033

AMA Style

Ramos Magna A, Zamora J, Allende-Cid H. Senti-Sequence: Learning to Represent Texts for Sentiment Polarity Classification. Applied Sciences. 2024; 14(3):1033. https://doi.org/10.3390/app14031033

Chicago/Turabian Style

Ramos Magna, Andres, Juan Zamora, and Hector Allende-Cid. 2024. "Senti-Sequence: Learning to Represent Texts for Sentiment Polarity Classification" Applied Sciences 14, no. 3: 1033. https://doi.org/10.3390/app14031033

APA Style

Ramos Magna, A., Zamora, J., & Allende-Cid, H. (2024). Senti-Sequence: Learning to Represent Texts for Sentiment Polarity Classification. Applied Sciences, 14(3), 1033. https://doi.org/10.3390/app14031033

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