Deep Learning for Multimodal Sentiment Analysis Integrating Text, Audio, and Video

In the fields of artificial intelligence (AI) and natural language processing (NLP), sentiment analysis (SA) has become increasingly popular. Demand for automating user sentiment analysis of goods and services is rising. Videos, as opposed to just text, are becoming more and more common online for sharing opinions. This has made the use of various modalities in SA, known as Multimodal Sentiment Analysis (MSA), a significant field of study. MSA uses the most recent developments in deep learning and machine learning at several phases, such as sentiment polarity detection and multimodal feature extraction and fusion, with the goal of reducing error rates and enhancing performance. Multiple data sources, such as text, audio, and video, are used into MSA to improve sentiment classification accuracy. Using cutting-edge deep learning algorithms, this work integrates text, audio, and video characteristics to examine multimodal sentiment analysis. After outlining a framework for feature extraction, fusion, and data pre-processing, we assess the framework's performance against industry-standard benchmarks.