Approximating Viewership of Streaming T.V Programs Using Social Media Sentiment Analysis

Abstract

Approximating viewership of a program is essential for broadcast networks and cable (TNT, AMC, and HBO) as they profit by selling advertising space during programming (airing of the show). With the advent and increased popularity of streaming services such as Netflix and Amazon Prime Video, the model for providing television content to viewers has fundamentally changed. However, this change in the delivery model for television programs has also made it more challenging to determine the actual viewership of television programs on streaming services. Because television programs are no longer aired in pre-determined timeslots, viewership shares cannot be determined. To make matters even more complicated, streaming services like Netflix and Amazon Prime Video are proprietary platforms. They have traditionally kept viewership numbers closely guarded and have not released them to the public. This paper proposes a methodology to approximate the viewership of streaming television shows. This is achieved by exploiting data from social networks, i.e., publicly available information and sentiment score derived from sentiment analysis on tweets published about television programs, using Random Forest classifier. The proposed methodology achieved 85% accuracy in predicting the viewership of the streaming shows.