Journal
NEURAL COMPUTING & APPLICATIONS
Volume 32, Issue 15, Pages 10809-10818Publisher
SPRINGER LONDON LTD
DOI: 10.1007/s00521-018-3442-0
Keywords
Aggression detection; Sentiment analysis; Machine learning; Convolutional neural network
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Cyberbullying and hate speeches are common issues in online etiquette. To tackle this highly concerned problem, we propose a text classification model based on convolutional neural networks for the de facto verbal aggression dataset built in our previous work and observe significant improvement, thanks to the proposed 2D TF-IDF features instead of pre-trained methods. Experiments are conducted to demonstrate that the proposed system outperforms our previous methods and other existing methods. A case study of word vectors is carried out to address the difficulty in using pre-trained word vectors for our short-text classification task, demonstrating the necessities of introducing 2D TF-IDF features. Furthermore, we also conduct visual analysis on the convolutional and pooling layers of the convolutional neural networks trained.
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