| In recent years, many models for predicting movie ratings have been proposed, focusing on utilizing movie reviews combined with sentiment analysis tools. In this study, we offer a different approach based on the emotionally analyzed concatenation of movie script and their respective reviews. 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. 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 results indicate that our proposed combination of features achieves a notable performance, similar to conventional approaches. |
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