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This is a Literature Review study.

August 22, 2025Brazilian Journal of BiometricsOpen Access

Emotion detection in text data: a comparative study of machine learning algorithms

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Authors

IHIkrama Dayyabu HayatuSharda University SSSachin SinghIndian Institute of Technology BHU MMM MuhammadSharda University

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Implication

This research evaluates emotion detection using machine learning algorithms, indicating that deep learning methods like BERT outperform traditional models.

Key Points

BERT achieved the highest accuracy of 0.8867 and an F1 Score of 0.8871, outperforming all other algorithms.
The study utilized a curated dataset to compare various machine learning algorithms, including CNN and SVM.
Traditional models like Naive Bayes showed the lowest performance metrics in emotion detection tasks.
Selecting appropriate models for emotion detection considers application requirements like interpretability and efficiency.

Cite This Study

Hayatu et al. (2025) studied this question.

synapsesocial.com/papers/68af59d7ad7bf08b1eade52f https://doi.org/10.28951/bjb.v43i4.786

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Implication

This research evaluates emotion detection using machine learning algorithms, indicating that deep learning methods like BERT outperform traditional models.

Emotion detection in text data: a comparative study of machine learning algorithms

Discussion

Member takes

Implication

Key Points

Cite This Study

Emotion detection in text data: a comparative study of machine learning algorithms

Discussion

Member takes

Implication

Key Points

Cite This Study

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