August 22, 2025Brazilian Journal of BiometricsOpen Access

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

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BERT outperformed other models in detecting emotions, achieving an accuracy of 0.8867 and F1 score of 0.8871.
Deep learning models like CNN and SVM also showed commendable performance, while traditional models had lower metrics.
The study utilized a curated dataset, comparing conventional machine learning models with deep learning techniques.
Significant implications for model selection based on specific application needs were highlighted, with future research suggested in bias reduction.

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Implication

This research compares machine learning and deep learning models for emotion detection in text, showing BERT's superior performance, suggesting tailored model selection for applications.

Cite This Study

Hayatu et al. (2025) studied this question.

synapsesocial.com/papers/68af76b97567bf4f94fefb8f https://doi.org/10.28951/bjb.v43i4.786

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Implication

This research compares machine learning and deep learning models for emotion detection in text, showing BERT's superior performance, suggesting tailored model selection for applications.

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

Key Points

Discussion

Implication

Cite This Study

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

Key Points

Discussion

Implication

Cite This Study

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