Predicting Movie-elicited Emotions from Dialogue in Screenplay Text: A Study on ''Forrest Gump''
Authors: Benedetta Iavarone, Felice Dell'Orletta (Scuola Normale Superiore, CNR-ILC, 2020)
Topic: Sentiment, Emotion, Irony, Hate

Abstract
We present a new dataset of sentences extracted from the movie Forrest Gump, annotated with the emotions perceived by a group of subjects while watching the movie. We run experiments to predict these emotions using two classifiers, one based on a Support Vector Machine with linguistic and lexical features, the other based on BERT. The experiments showed that contextual embeddings are effective in predicting human-perceived emotions.

Introduction
Emotional intelligence, described as the set of skills that contributes to the accurate appraisal, expression and regulation of emotions in oneself and in others AUTHOR, is recognised to be one of the facets that make us humans and the fundamental ability of human-like intelligence AUTHOR. Emotional intelligence has played a crucial role in numerous applications during the last years AUTHOR, and being able to pinpoint expressions of human emotions is essential to advance further in technological innovation. Emotions can be identified in many sources, among which there are semantics and sentiment in texts AUTHOR. In NLP, Sentiment Analysis already boasts many state-of-the-art tools that can accurately predict or classify the polarity of a text. However, real applications often need to go beyond the dichotomy positive-negative and identify the emotional content of a text with a finer granularity. Nevertheless, the task of predicting a precise emotion from text brings many challenges, mostly because there is a need of context: emotions can't be easily understood in isolation, as they are conveyed by a complex of explicit (e.g. speech) and implicit (e.g. gesture and posture) behavioural cues. Still, there has been an increasing interest in research for text-based emotion detection AUTHOR. In this work, we study how textual information extracted from the screenplay of a movie can be used to predict the emotions perceived by a group of people during the view of the movie itself. We create a new dataset of sentences extracted from the screenplay, annotated with six different perceived emotions and their perceived intensity and create a binary classification task to predict emotional elicitation during the view of the movie. We use two predicting models, with different kind of features that capture diverse language information. We determine which model and which kind of features are the best for predicting the emotions perceived by the subjects.

Conclusion
In this paper, we presented a dataset of sentences extracted from the movie Forrest Gump, annotated with the emotions that a group of subjects perceived while watching the movie, and we studied how to predict these emotions. To do so, we retrieved different kinds of features from the sentences pronounced by the characters of the movie. We showed that contextual embeddings extracted from the sentences can accurately predict specific emotions, even if the amount of text used for the prediction is very little. Instead, when predicting generic emotional elicitation, a larger amount of text is required for an accurate prediction. We also show that lexical, morpho-syntactic and syntactic aspects of the sentences cannot be used to infer emotional elicitation during the view of the movie.