Transforming Decision Making: Exploring the Influence of Natural Language Processing in Analyzing Social Media Interactions

Social media has become a vital aspect of our daily lives in the digital age, allowing for broad interactions among users. This review paper investigates the role of natural language processing (NLP) in realising the huge potential of social media interactions, as well as its impact on decision making. With the exponential growth of user-generated content on platforms like Twitter, Facebook , and Instagram, natural language processing (NLP) approaches have emerged as indispensable tools for analysing and extracting useful insights from this vast corpus of textual data. Researchers have begun to untangle the underlying patterns, sentiments, and ideas conveyed in social media interactions by applying various NLP approaches such as sentiment analysis, topic modelling, and opinion mining. This study gives a thorough review of the literature on how NLP-based textual analysis of social media interactions has altered decision-making processes in fields such as marketing, healthcare, politics, and customer service. It also investigates the potential problems and ethical concerns related with NLP-driven decision making, emphasising the importance of robust algorithms, data protection, and fairness. This review paper provides useful insights for scholars, practitioners, and policymakers alike by throwing light on the extraordinary potential of NLP in comprehending social media interactions and its impact on decision making, paving the way for future improvements in this dynamic sector.