Analysis and Exploitation of Twitter Data Using Machine Learning Techniques

In the present era, Internet is a well-developed technology that supports most of the social media analysis for various businesses such as marketing of a product, analysis of opinions of different customers, and advertising most of the brands. This gathered huge popularity among different users with a fresh way of interaction and sharing the thoughts about the things and materials. Hence, social media comprises of huge data that categorizes the attributes of Big Data, namely volume, velocity, and variety. This leads to the research work of this huge data related to different organizations and enterprise firms. To analyze the demands, customer's efficient data mining techniques are required. Nowadays, twitter is the one among the social networks which is dealing with millions of people posting millions of tweets. This paper exemplifies the data mining with machine learning techniques such as TF-TDF and clustering algorithms such as hierarchical clustering, k-means clustering, k-medoid clustering, and consensus clustering along with their efficiencies.