Comparative Analysis of Social Media Analytics in Bigdata Using Fuzzy C Mean (Fcm), K-Nearest Neighbour (Knn) And K-Means Algorithms

Abstract

One of the sites that generates a lot of data is social media. Data retrieved from social media is used to examine how the community behaves and perceives a specific occurrence or phenomenon. The amount of data used in the real-time situation grows linearly over time. It was found that one of the biggest data-producing sources, including Blogspot, Twitter, Facebook, Wikimapia, AIM, and YouTube, was growing at an uncontrollable rate. MapReduce is used in the Hadoop framework to carry out the job of filtering and aggregation as well as to maintain the effective storage structure. In order to gain insights from these platforms' sentimental research, social media analytics tools and technologies are used. In this essay, we talk about big data and MapReduce, which is crucial for using big data to analyze social networking site issues. By combining Hadoop and MapReduce, machine learning algorithms can improve the efficiency of big data. K-Nearest Neighbor (KNN), Fuzzy C Mean (FCM), and K-means algorithms.