Folksonomication: Predicting Tags for Movies from Plot Synopses Using Emotion Flow Encoded Neural Network
This work addresses the need for better movie retrieval and previewing by developing a domain-specific method for tag prediction, though it is incremental as it builds on existing neural network approaches with emotion flow integration.
The paper tackled the problem of automatically predicting tags for movies from plot synopses to improve recommendation engines and viewer expectations, achieving an 18% boost in tag learning by incorporating emotion flows into a neural network model.
Folksonomy of movies covers a wide range of heterogeneous information about movies, like the genre, plot structure, visual experiences, soundtracks, metadata, and emotional experiences from watching a movie. Being able to automatically generate or predict tags for movies can help recommendation engines improve retrieval of similar movies, and help viewers know what to expect from a movie in advance. In this work, we explore the problem of creating tags for movies from plot synopses. We propose a novel neural network model that merges information from synopses and emotion flows throughout the plots to predict a set of tags for movies. We compare our system with multiple baselines and found that the addition of emotion flows boosts the performance of the network by learning ~18\% more tags than a traditional machine learning system.