Social Media Data Analytics Using K-Means Clustering

In the recent technologically advanced world, internet serves as a vast repository of data. The exponential growth of online activities has resulted in an abundance of data, making it crucial for businesses to harness this information to gain meaningful insights for enabling effective decision-making. One significant source of data is social media platforms such as WhatsApp, Twitter, and Facebook. However, the sheer volume of data generated on these platforms presents challenges in terms of data handling and deriving valuable insights. The primary objective of this research work is to analyze social media data and extract valuable insights using advanced analytics techniques. By leveraging social media APIs, data will be collected from various platforms and the clustering algorithms are applied to uncover patterns, trends, and relationships within the dataset. The outcomes of the performed analysis offer valuable insights to drive their strategies towards achieving greater success. By utilizing social media data analytics, organizations gain a deeper understanding of their target audience, identify emerging market trends, and make data-driven decisions. This research aims to assist businesses in improving their strategies, enhancing customer engagement, and achieving better outcomes.