Abstract
A Film Synopsis Genre Classifier Based on Majority Vote
Highlights
Automatic classification of text is known to be a difficult task for a computer
We would like to precise that for this experiment, all textual information were taken from the english version of Wikipedia, the only language conflict we may have could be the translations of the synopsis into english
The movie signature is compared to the true genres, and the average accuracy is computed in the following way where MS represents the movie signature, GT the ground truth, N the number of test synopsis, lenSum the true positive predictions for a given synopsis, and SumttT the total amount of genres expected across the test set
Summary
We propose an automatic classification system of movie genres based on different features from their textual synopsis. Our system is first trained on thousands of movie synopsis from online open databases, by learning relationships between textual signatures and movie genres. It is tested on other movie synopsis, and its results are compared to the true genres obtained from the Wikipedia and the Open Movie Database (OMDB) databases. The results show that our algorithm achieves a classification accuracy exceeding 75%.
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