An emotion-based movie recommendation system which analyses subtitles using NLP to determine the emotional themes of a movie.
Aims to suggest movies to users using movie subtitles. This project focuses on the emotions of joy, sadness, anger, fear, love, and surprise. The usual movie recommendation systems rely on manual human classification or focus on capturing users’ emotions through facial recognition while hardcoding genres to specific emotions. Thus, this project focuses on classifying according to movies’ overall emotional themes as a range of emotions using natural language processing techniques.
Users will see an interactive graph that shows a film’s emotional development for its runtime. This graph will show users the emotional progression of the movie by emphasising different moments, such as joy, sadness, love, surprise, and more.
In a nutshell, this project will filter movies based on their emotional values and provide users with movies categorised based on the movie’s emotions. The user can view a graphical representation of the emotions that the movie will provide the user with throughout the movie. Users can discover movies that align with their desired emotional experience, ensuring a more accurate dynamic categorisation of films.