![]() ![]() The proposed features enhanced the accuracy by 11.9% and achieves an F1 Score of 75.1% in comparison to the state-of-the-art. ![]() A hybrid voting based classifier is created using Gradient Boosting, Random Forest, Support Vector Machine, Multilayer Perceptron, and Deep Learning classifiers to forecast the success of the movies in the development phase. Multiple time series are generated representing the sentiment of a story and plot topics that are collectively termed as “say” Story popularity. In this research, novel time series based features are proposed for “say” Story popularity in order to predict the movie success accurately. A movie plot is established during the development phase and it is crucial aspect for determining the movie success. Movie success prediction in the development phase is considered a challenging task due to the availability of very limited information.
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