Exploring Sentiment Analysis Approaches in a Public Agency Security News Dataset

Thiago Ruiz Lobo; Claudia Aparecida Martins

Exploring Sentiment Analysis Approaches in a Public Agency Security News Dataset

Thiago Ruiz Lobo, Claudia Aparecida Martins

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Abstract

As part of the institution’s 2024–2027 strategic plan, which includes the objective of understanding how the media portrays the organization to strengthen its public image, this paper investigates the application of deep learning algorithms in sentiment analysis of headline news about a public security institution. Four deep learning methods were applied in combination with three textual representations, resulting in twelve trained models. For each combination, a class-based analysis of the results was conducted. Models using BERT as the textual representation achieved strong performance, with an F1-score of approximately 90%.

Anthology ID:: 2026.propor-1.87
Volume:: Proceedings of the 17th International Conference on Computational Processing of Portuguese (PROPOR 2026) - Vol. 1
Month:: April
Year:: 2026
Address:: Salvador, Brazil
Editors:: Marlo Souza, Iria de-Dios-Flores, Diana Santos, Larissa Freitas, Jackson Wilke da Cruz Souza, Eugénio Ribeiro
Venue:: PROPOR
SIG:
Publisher:: Association for Computational Linguistics
Note:
Pages:: 878–888
Language:
URL:: https://aclanthology.org/2026.propor-1.87/
DOI:
Bibkey:
Cite (ACL):: Thiago Ruiz Lobo and Claudia Aparecida Martins. 2026. Exploring Sentiment Analysis Approaches in a Public Agency Security News Dataset. In Proceedings of the 17th International Conference on Computational Processing of Portuguese (PROPOR 2026) - Vol. 1, pages 878–888, Salvador, Brazil. Association for Computational Linguistics.
Cite (Informal):: Exploring Sentiment Analysis Approaches in a Public Agency Security News Dataset (Lobo & Martins, PROPOR 2026)
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PDF:: https://aclanthology.org/2026.propor-1.87.pdf

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@inproceedings{lobo-martins-2026-exploring,
    title = "Exploring Sentiment Analysis Approaches in a Public Agency Security News Dataset",
    author = "Lobo, Thiago Ruiz  and
      Martins, Claudia Aparecida",
    editor = "Souza, Marlo  and
      de-Dios-Flores, Iria  and
      Santos, Diana  and
      Freitas, Larissa  and
      Souza, Jackson Wilke da Cruz  and
      Ribeiro, Eug{\'e}nio",
    booktitle = "Proceedings of the 17th International Conference on Computational Processing of {P}ortuguese ({PROPOR} 2026) - Vol. 1",
    month = apr,
    year = "2026",
    address = "Salvador, Brazil",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.propor-1.87/",
    pages = "878--888",
    ISBN = "979-8-89176-387-6",
    abstract = "As part of the institution{'}s 2024{--}2027 strategic plan, which includes the objective of understanding how the media portrays the organization to strengthen its public image, this paper investigates the application of deep learning algorithms in sentiment analysis of headline news about a public security institution. Four deep learning methods were applied in combination with three textual representations, resulting in twelve trained models. For each combination, a class-based analysis of the results was conducted. Models using BERT as the textual representation achieved strong performance, with an F1-score of approximately 90{\%}."
}

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%0 Conference Proceedings
%T Exploring Sentiment Analysis Approaches in a Public Agency Security News Dataset
%A Lobo, Thiago Ruiz
%A Martins, Claudia Aparecida
%Y Souza, Marlo
%Y de-Dios-Flores, Iria
%Y Santos, Diana
%Y Freitas, Larissa
%Y Souza, Jackson Wilke da Cruz
%Y Ribeiro, Eugénio
%S Proceedings of the 17th International Conference on Computational Processing of Portuguese (PROPOR 2026) - Vol. 1
%D 2026
%8 April
%I Association for Computational Linguistics
%C Salvador, Brazil
%@ 979-8-89176-387-6
%F lobo-martins-2026-exploring
%X As part of the institution’s 2024–2027 strategic plan, which includes the objective of understanding how the media portrays the organization to strengthen its public image, this paper investigates the application of deep learning algorithms in sentiment analysis of headline news about a public security institution. Four deep learning methods were applied in combination with three textual representations, resulting in twelve trained models. For each combination, a class-based analysis of the results was conducted. Models using BERT as the textual representation achieved strong performance, with an F1-score of approximately 90%.
%U https://aclanthology.org/2026.propor-1.87/
%P 878-888

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Markdown (Informal)

[Exploring Sentiment Analysis Approaches in a Public Agency Security News Dataset](https://aclanthology.org/2026.propor-1.87/) (Lobo & Martins, PROPOR 2026)

Exploring Sentiment Analysis Approaches in a Public Agency Security News Dataset (Lobo & Martins, PROPOR 2026)

ACL

Thiago Ruiz Lobo and Claudia Aparecida Martins. 2026. Exploring Sentiment Analysis Approaches in a Public Agency Security News Dataset. In Proceedings of the 17th International Conference on Computational Processing of Portuguese (PROPOR 2026) - Vol. 1, pages 878–888, Salvador, Brazil. Association for Computational Linguistics.