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Machine Learning Techniques for Automated Movie Genre Classification Tool

Everyone is of different kinds in the Real World with liking or preferences of some specific genres. Genre reflects the style or brand of the story, novel, poetry, film, or music. Liking of the specific genre is inbuilt in human beings. It is a spontaneous identity of the human being. Moreover, identification of the sentiment of a person becomes easier from the several feedbacks posted on social media. The human being, as an author, artist, novelist, dancer, singer, or film producer, cannot be a jack of all trades. So, genre plays an important role in any creation, and based on the reviews of these creations, human sentiment analysis is easily possible. This paper proposes a multi-level genre-based textual sentiment analysis. This analysis is automated with various inputs of reviews and classifies nine categorical genres as output. The sample online input dataset is utilized, and three different machine learning algorithms are used to implement the proposed automated genre-based textual sentiment analysis framework. This paper applies machine learning techniques to calculate the optimal score for each genre based on the classification report. Based on the training accuracy and testing accuracy, the best algorithm is also recognized as an automated genre classification tool.