Predicting Quality and Popularity of a Movie From Plot Summary and Character Description Using Contextualized Word Embeddings
Jung-Hoon Lee, You-Jin Kim, Yun-Gyung Cheong · 2020 IEEE Conference on Games (CoG) · 2020
Narrative is an essential factor that makes games more enjoyable. However, predicting the story quality has been challenging for decades. In this paper, we propose to use contextual word embedding models such as BERT and ELMo, for predicting a story's success in terms of quality and popularity by using the story text only. Since deep learning models generally require extensive data, we conducted experiments to test the efficacy of our proposed model by leveraging the movie plot summaries. We present the results of the evaluations and conclude with discussions.