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

arXiv: https://arxiv.org/abs/1808.04943 · HTML: https://mlsanity.com/paper/1808.04943/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Aug 2018.

## Built on by (most cited first, 4 of 4)

- [FakeFlow: Fake News Detection by Modeling the Flow of Affective Information](https://mlsanity.com/paper/2101.09810/) (2021)
- [Situation and Behavior Understanding by Trope Detection on Films](https://mlsanity.com/paper/2101.07632/) (2021)
- [Demystifying Visual Features of Movie Posters for Multi-Label Genre Identification](https://mlsanity.com/paper/2309.12022/) (2023)
- [Unraveling Movie Genres through Cross-Attention Fusion of Bi-Modal Synergy of Poster](https://mlsanity.com/paper/2410.19764/) (2024)

## Abstract (arXiv)

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.