Published Paper Details:

Sentiment analysis and emotion recognition play a pivotal role in understanding human communication and interaction, with applications spanning from customer feedback analysis to human-computer interaction. In recent years, deep learning techniques have demonstrated remarkable success in these domains, particularly when incorporating speech features and transcriptions. This review delves into the state-of-the-art systems that employ deep learning for sentiment analysis and emotion recognition, focusing on those leveraging both speech features and transcriptions as complementary modalities. The review begins by providing an overview of the theoretical foundations of sentiment analysis and emotion recognition and the evolution of deep learning in these fields. It highlights the significance of multimodal approaches that fuse speech features and transcriptions to improve accuracy and robustness. The paper discusses various deep learning architectures, including recurrent neural networks (RNNs), convolutional neural networks (CNNs), and more recent models like transformer-based architectures, and their utilization in sentiment analysis and emotion recognition tasks. Special attention is given to pre-trained models and transfer learning techniques that have proven to be highly effective in reducing the need for large labeled datasets. The importance of benchmark datasets and evaluation metrics for assessing the performance of sentiment analysis and emotion recognition models is addressed. The paper concludes with an exploration of potential future directions, including enhanced multimodal fusion techniques, cross-modal sentiment analysis, and the integration of these models into real-world applications. By reviewing and synthesizing the current state of deep learning-based sentiment analysis and emotion recognition systems using speech features and transcriptions, this paper aims to provide insights into the progress made, challenges faced, and the promising avenues for future research in these critical fields of natural language processing and affective computing.