Forecasting Television Ratings with Trends and Social Information
Date Issued
2014
Date
2014
Author(s)
Yan, Yu-An
Abstract
Because of rising of web services such as search engine and social network, there are more and more information we can extract from the internet. In this paper, we propose a method to utilize information from the internet to enhance the performance of TV rating prediction models. We extract features such as trends of search query, posts and opinion on Facebook; apply two common models for TV rating prediction, Support Vector Regression and Gaussian Process Regression, with these features, and demonstrate that they can be useful for TV rating prediction. Furthermore, we compare the performance of our model under two different settings, namely forecasting and nowcasting, and provide several discussions.
Subjects
電視收視率預測
支持向量機回歸
高斯過程回歸
時間序列
趨勢
社群資訊
Type
thesis
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