Real-time Twitter Trending Topics Detection and Sentiment Analysis Using Data Mining Technique

A social data revolution has been sparked by recent developments in scalable data management systems, trustworthy social web technologies, and data mining tools. This change has opened up a plethora of opportunities for improving data analysis techniques and social data science research in conjunction with the advancement of data mining tools. It is also in tandem with the evolution of data mining techniques. It also coincides with the development of data mining methods. Despite the abundance of data available, doing comprehensive analyses with social datasets poses significant methodological challenges that can obstruct productive research. In this research article, we introduce techniques for efficiently collecting trending topics in real time from the social web, with a focus on sentiment analysis of people using Twitter data. Furthermore, we propose several effective data cleaning methods specifically tailored for social media data to increase the accuracy of sentiment analysis. This allencompassing strategy tackles major obstacles to using social data for insightful study and analysis, expanding our understanding of the mechanisms at work in the digital social environment.