Name
Title | ||
|---|---|---|
Folksonomication: Predicting Tags for Movies from Plot Synopses using Emotion Flow Encoded Neural Network. |
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. |
Year | Venue | DocType |
|---|---|---|
2018 | COLING | Journal |
Volume | Citations | PageRank |
abs/1808.04943 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sudipta Kar | 1 | 5 | 4.14 |
| Suraj Maharjan | 2 | 41 | 6.99 |
| Thamar Solorio | 3 | 432 | 55.65 |