Abusive text detection using neural networks

Neural network models have become increasingly popular for text classification in recent years. In particular, the emergence of word embeddings within deep learning architectures has recently attracted a high level of attention amongst researchers. In this paper, we focus on how neural network models have been applied in text classification. Secondly, we extend our previous work [4, 3] using a neural network strategy for the task of abusive text detection. We compare word embedding features to the traditional feature representations such as n-grams and handcrafted features. In addition, we use an off-the-shelf neural network classifier, FastText[16]. Based on our results, the conclusions are: (1) Extracting selected manual features can increase abusive content detection over using basic ngrams; (2) Although averaging pre-trained word embeddings is a naive method, the distributed feature representation has better performance to ngrams in most of our datasets; (3) While the FastText classifier works efficiently with fast performance, the results are not remarkable as it is a shallow neural network with only one hidden layer; (4) Using pre-trained word embeddings does not guarantee better performance in the FastText classifier.