Violence Rating Prediction from Movie Scripts

Abstract

Violent content in movies can influence viewers’ perception of the society. For example, frequent depictions of certain demographics as perpetrators or victims of abuse can shape stereotyped attitudes. In this work, we propose to characterize aspects of violent content in movies solely from the language used in the scripts. This makes our method applicable to a movie in the earlier stages of content creation even before it is produced. This is complementary to previous works which rely on audio or video post production. Our approach is based on a broad range of features designed to capture lexical, semantic, sentiment and abusive language characteristics. We use these features to learn a vector representation for (1) complete movie, and (2) for an act in the movie. The former representation is used to train a movie-level classification model, and the latter, to train deep-learning sequence classifiers that make use of context. We tested our models on a dataset of 732 Hollywood scripts annotated by experts for violent content. Our performance evaluation suggests that linguistic features are a good indicator for violent content. Furthermore, our ablation studies show that semantic and sentiment features are the most important predictors of violence in this data. To date, we are the first to show the language used in movie scripts is a strong indicator of violent content. This offers novel computational tools to assist in creating awareness of storytelling.

Joint Estimation and Analysis of Risk Behavior Ratings in Movie Scripts

“…Most similar to our work are efforts in predicting a single movie-level rating from language either in movie scripts (Martinez et al, 2019;Shafaei et al, 2019) or in transcripts (Mohamed and Ha, 2020). These works explore the use of recurrent neural networks (RNN) over sequences of vector representations, each composed by the concatenation of lexical, semantic and sentiment features, to learn a movie representation from which the target rating is predicted.…”

“…Previous works show the benefits of including lexical features that capture the expressed sentiment characteristics from language for media content prediction tasks (Martinez et al, 2019;Shafaei et al, 2019). However, most approaches to sentiment analysis on movie scripts rely on manuallyconstructed sentiment lexica (e.g., Lapata 2018, 2015).…”

“…Categorizing a movie indirectly assists in shaping the characters and the story of the movie, and determines the plot and best setting to use. Thus, movie genre contains information on the type of content one could expect in a movie (especially for the case of violent content (Martinez et al, 2019)). Thus, our models include movie genre as an additional feature.…”

“…To evaluate the performance of our model, and directly compare it to previous work, we manually align a subset of 989 movie scripts from our dataset to the content ratings found in (Martinez et al, 2019). These ratings come from Common Sense Media (CSM), a non-profit organization that promotes safe technology and media for children 6 .…”

“…We follow a procedure similar to that described in Martinez et al (2019), which discards scene headers, actions and transitions to represent a movie script as a sequence of actors speaking one after another. This leads to a natural formulation of a sequence learning model for capturing the dialog narrative using recurrent neural networks.…”