Folksonomication: Predicting Tags for Movies from Plot Synopses Using Emotion Flow Encoded Neural Network

TL;DR

This paper introduces a neural network model that predicts movie tags from plot synopses by incorporating emotion flow analysis, significantly improving tag prediction accuracy for recommendation systems.

Contribution

The study presents a novel neural network architecture that combines plot synopsis analysis with emotion flow encoding to enhance movie tag prediction accuracy.

Findings

Emotion flow encoding improves tag prediction by ~18%.

The proposed model outperforms traditional machine learning baselines.

Incorporating emotional dynamics enhances understanding of movie content.

Abstract

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.