Social network usage is growing exponentially in the most up-to-date decade; though social networks are becoming increasingly popular every day, many users are continuously active social network users. Using Twitter, LinkedIn, Facebook, and other social media sites has become the most convenient way for people. There is an enormous quantity of data produced by users of social networks. The most common part of modern research analysis is instrumental for many social network analysis applications. However, people actively utilize social networking sites and diverse uses of these sites. social media sites handle an immense amount of knowledge and answer these three computational problems, noise, dynamism, and scale. Semantic comprehension of the document, image, and video exchanged in a social network was also an essential topic in network analysis. Utilizing data processing provides vast datasets such as averages, laws, and patterns to discover practical knowledge. Using social media, data analysis was primarily used for machine learning, analysis, information extraction, statistical modelling, data preprocessing, and data interpretation processes. This research intentions to deliver an inclusive overview of social network research and application analyze state-of-the-art social media data analysis methods by reviewing basic concepts, social networks and elements social network research is linked to. Semantic ways of manipulating text in social networks are then clarified, and literature discusses studies before on these themes. Next, the evolving methods in research on social network analysis are discussed, especially in analyzing semantic text on social networks. Finally, subjects and opportunities for future research directions are explained.
"Semantic Text Analysis on Social Networks and Data Processing: Review and Future Directions,"
Information Sciences Letters: Vol. 11
, PP -.
Available at: https://digitalcommons.aaru.edu.jo/isl/vol11/iss5/6