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This research focuses on detecting and classifying emotions in text to enhance human-computer interaction.

May 7, 2026Open Access

Emotion Detection from Text using CNN, LSTM and Hybrid CNN-LSTM Deep Learning Model

Key Points

This research focuses on detecting and classifying emotions in text to enhance human-computer interaction.
Developed deep learning models including CNN and LSTM for emotion classification.
Implemented traditional machine learning approaches like Logistic Regression and Naive Bayes for comparison.
Used text preprocessing techniques like stop words removal and padding for data preparation.
Deep learning models outperformed traditional models in emotion classification.
The hybrid CNN-LSTM model achieved the best performance for multi-class emotion detection.
Accuracy, precision, recall, F1-score, and confusion matrix were used for performance evaluation.

Abstract

Text classification has numerous applications in real world scenarios. Emotion detection from text is one of the vital tasks within natural language processing, which has gained significant attention of researchers over the years. In this study, emotions are detected and classified for better human–computer interaction, sentiment analysis, health management, and smart chattingbots. A number of deep learning models are developed and outperform the traditional models for the classification. Con- volutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and hybrid of CNN and LSTM are used for emotion classification. Furthermore, two traditional machine learning ap- proaches including Logistic Regression and Naive Bayes are also implemented for comparison purpose. Preprocessing is a very important step for a good model. Text normalization, stop words removal, tokenization, and padding are used for data preparation. Word embeddings, specifically pre-trained word2vec, are used to capture the semantic relationship of text features learned from deep learning models. The performance evaluation of these models is done using accuracy, precision, recall, F1-score, and confusion matrix. The experimental results show that the deep learning models have outperformed the traditional models. The hybrid CNN-LSTM model achieved the best results to classify emotions in multi-class problem.

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