What question did this study set out to answer?

The primary aim is to enhance emotion detection in text by integrating LSTM with RoBERTa.

January 21, 2026Open Access

A novel hybrid model for emotion detection in text through sequential and transformer-based approaches: LSTM enhanced RoBERTa (LER)

Key Points

The primary aim is to enhance emotion detection in text by integrating LSTM with RoBERTa.
Developed a hybrid model named LSTM Enhanced RoBERTa (LER)
Tested the model on the ISEAR emotion dataset
Evaluated performance using metrics like precision, recall, and F1-score
Achieved 88% accuracy, outperforming many baseline models
Effectively detected complex emotional cues in context-sensitive applications

Abstract

Abstract Text emotion detection is an essential task in Natural Language Processing (NLP), with applications in customer support automation, diagnosing mental health, and social media analysis. Yet, precise emotion detection is a difficult problem as human emotional states are subtle, ambiguous, and contextual. Current models typically fail to fully grasp these subtleties. To overcome these limitations, the current study proposes a new hybrid architecture, LSTM Enhanced RoBERTa (LER), that combines the sequential learning ability of Long Short-Term Memory (LSTM) networks with the deep contextual knowledge provided by the RoBERTa transformer model. The LER model proposed here is tested on the popular ISEAR emotion dataset and registers an impressive accuracy of 88%, which surpasses many robust baseline models. The model’s performance is evaluated on standard metrics, such as precision, recall, and F1-score. The results show that the hybrid approach efficiently detects intricate emotional cues, thus improving the state of emotion detection for real-world, context-sensitive applications.

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