ABSTRACT
The perception of reality TV shows from level-headedness in cinema. It is a design that common people live in, quite often in deliberately manufactured situations, where examiners or judges their opinions, performance or talent. These shows usually raise competition and provide money as prizes. These shows will gather viewer’s online postings, mobile messages and analyze the same to increase TRP ratings. Now-a-days most television channels organizing reality show which are being telecasted especially in fields such as dancing, singing, and drama. In this work, we compile all online postings from social networks in a CSV file. These are further classified into different classes based on their opinion on a particular TV show which contains viewer’s personal profile based on location and comment. Based on their postings and opinions, the TV Show popularity will be rated accordingly. We develop a model to exploit the viewer’s opinions for predicting social actions (e.g., users’ behaviours, opinions, preferences or interests) and discovering their field of interest. We propose a fuzzy based clustering algorithm with regression analysis to learn the model parameters in an efficient manner. We performed experiments on two real-world datasets to demonstrate the validity and competitiveness of our approach.
