Enhancing Natural Language Processing with Deep Learning for Sentiment Analysis

Natural language processing (NLP) aids in the advancement of intelligent machines through its emphasis on etymologically grounded human-PC connections and a greater understanding of the human language. The demand and necessity to implement information-driven methodologies for automating semantic analysis have increased due to ongoing advancements in processing power and the accessibility of vast quantities of phonetic data. The application of deep learning methods has facilitated significant progress in fields including computer vision, programmed discourse recognition, and natural language processing, which has led to the pervasive adoption of information-driven techniques. This study investigates calculations for unimodal and multimodal sentiment analysis in interpersonal organisations and develops two models, one for message sentiment analysis and the other for picture-message multimodal sentiment analysis in informal communities. The two models demonstrated their electiveness by surpassing the benchmark models by 4.45% and 5.2%, respectively, when evaluated against the current models on distinct datasets.