Mining the Attitude of Social Network Users using K-means Clustering

Abstract

Social media is the collective of online communications channels dedicated to community-based input, interaction, content-sharing and collaboration. Social media has become a central point of a person’s daily life for many people around the world with the ability to be connected to these sites through access to cellphones, tablets, and computers. The ease of sharing information has allowed people to keep in contact with friends and family and keep them updated on life changes, views of various subjects, collaborate on projects, and much more. It has also made it possible for groups or individuals who can unlike or retweet your posts. User’s opinions may be any of forms such as Text, Image, Audio, and video. In this work, take Text format to mining the users’ attitude for the social network. The user may tweet a comment using any of the social media to a particular topic from different place and time. K-Means Clustering is the task of grouping a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups. Popular notions of clusters include groups with small distances among the Cluster members, dense areas of the data space, and intervals of particular statistical distributions. Using clustering techniques we group the similar and dissimilar of users’ attitude.