Comparison of Machine Learning Techniques for Multi-label Genre Classification

We compare classic text classification techniques with more recent machine learning techniques and introduce a novel architecture that outperforms many state-of-the-art approaches. These techniques are evaluated on a new multi-label classification task, where the task is to predict the genre of a movie based on its subtitle. We show that pre-trained word embeddings contain ’universal’ features by using the Semantic-Syntactic Word Relationship test. Furthermore, we explore the effectiveness of a convolutional neural network (CNN) that can extract local features, and a long short term memory network (LSTM) that can find time-dependent relationships. By combining a CNN with an LSTM we observe a strong performance improvement. The technique that performs best is a multi-layer perceptron, with as input the bag-of-words model.