Sentiment analysis for TV show popularity prediction: case of Nation Media Group’s NTV

Abstract

Media-houses play a vital role in informing the masses on the issues that matter. They are also a source of entertainment for many households. In Kenya, the public depends on media largely for entertainment and educational purposes. However, many media-houses find it difficult to make decisions on what the viewers actually wish to watch. This makes the media-houses to be in the dark, unaware of what viewers want and making decisions based on perceptions rather than data. Most of the analytic tools used by media-houses in Kenya are used to provide insights on website-related activities such as site visits, number of article reads and read-depths. This is not a good way of measuring popularity and does not create a true reflection of how an audience perceives a given product. In this study, we propose a predictive model that uses sentiment analysis to determine the popularity of a given TV show. This enables accurate decisions to be made based on the viewership trends over a specific period of time. Natural Language Processing is used to perform sentiment analysis on tweets derived from Twitter. This solution involved tweets being derived from the social site Twitter through the use of the Twitter API. Once fetched, the tweets had their polarity measured through the use of a lexicon dictionary in order to remove neutral tweets. These tweets were then be labelled as either positive or negative using the Support Vector Machine classifier. Then the overall popularity score of a movie was calculated. The solution was able to not only show the polarity of derived tweets, but also assign overall popularity scores showing how positive or negative a TV show is.