To capture the shape of stories is crucial forunderstanding the mind of human beings. In this research, we use word emdeddings methods, a widely used tool in natural language processing and machine learning, in order to quantify and compare emotional arcs of stories over time. Based on trained Google News word2vec vectors and film scripts corpora (N =1109), we form the fundamental building blocks of story emotional trajectories. The results demonstrate that there exists only one universal pattern of story shapes in movies. Furthermore, there is a positivity and gender bias in story narratives. More interesting, the audience reveals a completely different preference from content producers.
The manuscript titled The Hidden Shape of Stories Reveals Positivity Bias and Gender Bias authored by Huimin Xu et al. has already appeared on arxiv. Please check this link https://arxiv.org/pdf/1811.04599.pdf. So far, it still lacks theoretical underpinnings, and your comments are welcome.