A Comparative Study of CNNs and DNNs for Emotion Detection from text using TF-IDF

With several applications in fields including sentiment analysis, human-computer interaction, and mental health monitoring, emotion identification from text has emerged as a crucial job in natural language processing. The task of emotion recognition from textual data is examined using a comparison of Convolutional Neural Networks (CNNs) and Deep Neural Networks (DNNs). We investigate and assess the performance of these two well-known neural network designs by using Term Frequency-Inverse Document Frequency (TF-IDF) as a feature extraction technique. The widely-known ISEAR dataset is one of the benchmark datasets used in the study. The preparation of text data and use of TF-IDF to convert the text into useful numerical representations serve as the basis for the comparison analysis. Following this, we design and implement tailored CNN and DNN models, each optimized for the specific task of emotion detection. Our findings show that the CNN and DNN models have different advantages and disadvantages. While DNNs offer flexibility and resilience in modeling com-plicated relationships, CNNs excel at capturing local characteristics and spatial hierarchies within the text. The comparative evaluation considers several metrics, including recall, accuracy, precision, and F1-score, to provide a more in-depth knowledge of the performance of the models. We al-so investigate the performance impact of model complexity and hyperparameter tuning, offering insights into the best configuration for both architectures in the context of emotion detection. The results of this study provide important new understandings regarding the relative merits and applicability of CNNs and DNNs for emotion recognition from text, employing TF-IDF as a feature extraction technique.The paper ends with concrete suggestions for researchers and practitioners trying to choose the best architecture for certain applications and future possibilities for improving the efficacy and efficiency of emotion detection from text using neural networks