This information has already been exploited in many applications including movie recommendations and genre classification systems.
In sites like “Metacritic” or “Rotten Tomatoes” reviews, analyses and criticisms are collected to create an average estimation of how popular and well-received a movie is. It is very common nowadays, for the audience and critics alike to express their enjoyment of a movie or lack thereof, in the form of online reviews. In order to achieve this goal, the script needs to be written in a manner that captures the appropriate sentiments and allows the actors to portray it in their performances. Movies usually contain scenes where emotions alter dynamically, between happiness and sadness, calmness and anger in order to aid the narrative progression, while some works are characterized by an overarching emotional ‘weight’, such as sadness in a tragedy-based film. It is probably the most immediate point of resonance and communication with the audience. The script’s “emotional charge” is usually a tool to achieve the aforementioned goal or a byproduct of the process- and indeed a very important and powerful one. In principle, they are a storytelling device where the screenwriter is trying to convey something meaningful. Movie scripts are an interesting source of text, due to the diverse display of sentiments expressed in them. The results indicate that our proposed combination of features achieves a notable performance, similar to conventional approaches. We collected a dataset consisting of 747 movie scripts and 78.000 reviews and recreated many conventional approaches for movie rating prediction, including Vector Semantics and Sentiment Analysis techniques ran with a variety of Machine Learning algorithms, in order to more accurately evaluate the performance of our model and the validity of our hypothesis. The rationale behind this model is that if the emotional experience described by the reviewer corresponds with or diverges from the emotions expressed in the movie script, then this correlation will be reflected in the particular rating of the movie. In this study, we offer a different approach based on the emotionally analyzed concatenation of movie script and their respective reviews. In recent years, many models for predicting movie ratings have been proposed, focusing on utilizing movie reviews combined with sentiment analysis tools.
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