2018/08/15 by Sudipta Kar, Kar, Sudipta, Suraj Maharjan +3
Computer Science · #Computation and Language (cs.CL) #Data Visualization and Analytics #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis #Video Analysis and Summarization
paper · pdf · doi:10.48550/arxiv.1808.04943
openalex created_date 2018/07/19 · openalex publication_date 2018/08/15 · openalex updated_date 2026/07/28
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.