Emotion Recognition In Text And Image Using LSTM And CNN Architecture

Authors: Dr. Gaurav Aggarwal, Narinder Yadav

Abstract: Emotion recognition in texts and images is an area under rapid development at the moment, as advancements in deep learning, natural language processing, and computer vision seem to facilitate human-computer interaction, mental health monitoring, and many more aspects of sentiment analysis. The study seeks to propose a hybrid model integrating LSTM networks for text and a CNN-based architecture for image data in order to handle imbalanced datasets, sarcasm detection, and feature extraction from graphical content. The multimodal fusion will help the proposed framework capture the nuanced emotional signals of both modalities, providing a more holistic understanding of human emotions. This is evaluated on publicly available datasets that show improvements in terms of accuracy, precision, and F1-score compared to traditional approaches. But this work goes well beyond the technical boundaries of emotion recognition and raises ethical concerns and demands privacy and fairness in applications. More importantly, emotion-aware systems have transformative potential from customer sentiment analysis to adaptive learning environments and support for mental health.