What question did this study set out to answer?

To improve emotion detection in text by utilizing sentence-level emotion enrichment with transformer models. The aim is to capture nuanced emotions better than existing methods.

July 29, 2026ACM Transactions on Asian and Low-Resource Language Information Processing

Enhancing Text-Based Emotion Detection in Turkish and English: A Sentence-Level Enrichment Approach Using BERT and DistilBERT

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Authors

SMSenem Kumova Metın HUHande Aka Uymaz

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Overview

Randomized trial demonstrates improved emotion detection accuracy in Turkish and English, suggesting a valuable approach for emotion processing systems.

Key Points

To improve emotion detection in text by utilizing sentence-level emotion enrichment with transformer models. The aim is to capture nuanced emotions better than existing methods.
Employed three emotion-enrichment methods for sentence vectors.
Evaluated performance using various machine learning classifiers.
Utilized evaluation metrics like in-category similarity and classification performance.
Sentence-level enrichment shows significant improvement in classification performance.
Emotion-enriched sentence representations outperform original pre-trained model vectors.
Better in-category similarity results were achieved with enriched sentences.

Cite This Study

Metın et al. (2026) studied this question.

synapsesocial.com/papers/6a69a28ac8da07d9defa60bd https://doi.org/10.1145/3833411

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July 29, 2026ACM Transactions on Asian and Low-Resource Language Information Processing

Enhancing Text-Based Emotion Detection in Turkish and English: A Sentence-Level Enrichment Approach Using BERT and DistilBERT

View Full Paper

Ask AI

Bookmark

Authors

SMSenem Kumova Metın HUHande Aka Uymaz

Discussion

Member takes

Overview

Randomized trial demonstrates improved emotion detection accuracy in Turkish and English, suggesting a valuable approach for emotion processing systems.

Key Points

To improve emotion detection in text by utilizing sentence-level emotion enrichment with transformer models. The aim is to capture nuanced emotions better than existing methods.
Employed three emotion-enrichment methods for sentence vectors.
Evaluated performance using various machine learning classifiers.
Utilized evaluation metrics like in-category similarity and classification performance.
Sentence-level enrichment shows significant improvement in classification performance.
Emotion-enriched sentence representations outperform original pre-trained model vectors.
Better in-category similarity results were achieved with enriched sentences.

Cite This Study

Metın et al. (2026) studied this question.

synapsesocial.com/papers/6a69a28ac8da07d9defa60bd https://doi.org/10.1145/3833411

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