In the realm of human-computer interaction, emotion recognition and sentiment analysis play a pivotal role in understanding user experiences and enhancing communication between individuals and machines. This research paper delves into the integration of text and audio-based approaches for emotion recognition and sentiment analysis using advanced Artificial Intelligence (AI) techniques. By harmonizing these modalities, we aim to develop a more comprehensive and accurate understanding of users' emotional states and sentiments. The proposed methodology leverages Natural Language Processing (NLP) techniques for processing textual data and audio signal processing methods for analyzing audio inputs. Our AI-driven
framework employs machine learning algorithms, including deep learning models, to capture nuanced emotions and sentiments expressed in both text and audio content. The synergy between these modalities promises to enrich the accuracy and reliability of emotion recognition and sentiment analysis systems. Through experiments and evaluations on diverse datasets, we showcase the effectiveness of the hybrid approach in capturing complex emotional cues. The results demonstrate enhanced accuracy and cross- modal validation, highlighting the potential for real-world applications in fields such as customer sentiment analysis, virtual assistants, and affective computing. This article introduces a hybrid model that combines
machine learning and deep learning methodologies for the purpose of emotion identification in text. The model leverages Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) as its deep learning components, while also incorporating Support Vector Machines (SVM) as a machine learning technique. To assess the effectiveness of this approach, the model's performance is gauged across three distinct types of datasets: sentences, tweets, and dialogs. Notably, the proposed hybrid model achieves an impressive accuracy rate of 85.11%.