Predicting Movie Genres Based on Plot Summaries

01/15/2018
by   Quan Hoang, et al.
0

This project explores several Machine Learning methods to predict movie genres based on plot summaries. Naive Bayes, Word2Vec+XGBoost and Recurrent Neural Networks are used for text classification, while K-binary transformation, rank method and probabilistic classification with learned probability threshold are employed for the multi-label problem involved in the genre tagging task.Experiments with more than 250,000 movies show that employing the Gated Recurrent Units (GRU) neural networks for the probabilistic classification with learned probability threshold approach achieves the best result on the test set. The model attains a Jaccard Index of 50.0 of 0.56, and a hit rate of 80.5

READ FULL TEXT
research
11/13/2019

Structured Sparsification of Gated Recurrent Neural Networks

Recently, a lot of techniques were developed to sparsify the weights of ...
research
11/01/2018

AttentionXML: Extreme Multi-Label Text Classification with Multi-Label Attention Based Recurrent Neural Networks

Extreme multi-label text classification (XMTC) is a task for tagging eac...
research
06/01/2020

A multimodal approach for multi-label movie genre classification

Movie genre classification is a challenging task that has increasingly a...
research
07/02/2020

A Novel BGCapsule Network for Text Classification

Several text classification tasks such as sentiment analysis, news categ...
research
02/14/2018

Classifying movie genres by analyzing text reviews

This paper proposes a method for classifying movie genres by only lookin...
research
03/22/2017

Classification-based RNN machine translation using GRUs

We report the results of our classification-based machine translation mo...
research
05/23/2017

Grounded Recurrent Neural Networks

In this work, we present the Grounded Recurrent Neural Network (GRNN), a...

Please sign up or login with your details

Forgot password? Click here to reset