AI-DRIVEN SENTIMENT ANALYZER

The main application of natural language processing is to analyze the author’s sentiment within a given context. Sentiment analysis (SA) aims to determine the accuracy of the underlying emotion expressed in the context. It has been employed in various fields such as stock market prediction, social media analysis of product reviews, psychology, judiciary, forecasting, disease prediction, agriculture, and more. Numerous researchers have made significant contributions in these domains, yielding valuable outcomes that facilitate a quick understanding of the overall summary. Additionally, sentiment analysis plays a crucial role in comprehending genuine feedback shared across platforms like Amazon, TripAdvisor, and others. This comprehensive survey aims to review some of the most important research conducted to date and provide an overview of sentiment analysis models within the domain of emotion AI-driven SA. Furthermore, this work explores sentiment analysis applied to different types of data, including images and speech. Visual sentiment analysis endeavors to understand the emotional impact of images on individuals. Despite being a relatively new field, a wide range of techniques has been developed to address various data sources and topics, resulting in a substantial body of research. Therefore, this paper considers a structured formalization of the problem that is commonly employed for text evaluation and discusses its applicability in the context of visual sentiment analysis. The paper also highlights recent challenges and examines progress towards more sophisticated systems and practical applications, offering a summary of the insights gained from this study.