A Study on Domestic Drama Rating Prediction

Abstract

Audience rating competition in the domestic drama market has increased recently due to the introduction of commercial broadcasting and diversification of channels. There is now a need for thorough studies and analysis on audience rating. Especially, a drama rating is an important measure to estimate advertisement costs for producers and advertisers. In this paper, we study the drama rating prediction models using various data mining techniques such as linear regression, LASSO regression, random forest, and gradient boosting. The analysis results show that initial drama ratings are affected by structural elements such as broadcasting station and broadcasting time. Average drama ratings are also influenced by earlier public opinion such as the number of internet searches about the drama.

Relation between early e-WOM and average TV ratings

“…One is the ratings of the first episode of a show (FirstRatings). Kang et al (2015) found that ratings of the first episode had the largest influence on average ratings among the variables in the analysis of Korean dramas. Choi et al (2017) showed that ratings of the first episode were an important indicator to predict the average ratings.…”