AI Driven Sentiment Analysis for Social Media Data

Sentiment analysis (SA) is a subject of ongoing text mining research. The subjectivity, emotions, and viewpoints of a text are handled algorithmically by sentiment analysis. This survey research examines the most recent development in this field in great detail. This paper examines and provides a quick overview of various recently suggested algorithm enhancements. Based on their contributions to the various sentiment analysis approaches, these articles are categorized into various categories. Sentiment analysis may be utilised to extract information related to thoughts and feelings from human-generated textual data. Users have posted a significant quantity of unprocessed material to social networking sites in the shape of speech, videos, photographs, and audio. Global catastrophes including heatwaves, earthquakes, cyclones, floods and bushfires are having an extraordinary impact on social media users' life. They frequently write how pessimistic they find the catastrophic scenarios at their target area to be. Politicians and strategic planners must prioritise comprehending location-specific attitudes towards crisis situations. Data may be turned into valuable knowledge by using sentiment analysis. The primary goal of the questionnaire is to provide a concise, almost complete picture of Sentiment Analysis methods and associated topics. The comprehensive categories of several recently published papers and the explanation of the present state of research in sentiment evaluation as well as related fields are the key contributions made by this study.