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Malicious Comment Classification using Bidirectional LSTM and Convolutional Neural Networks
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Social media has become an important part of our daily lives due to the recent trends. Sites are flooding with tons of posts and opinions of people and social media communication has been on the rise. Although this has mostly been a boon to us, unfortunately it involves enormous dangers, since online texts with high toxicity can cause personal attacks, online harassment and bullying behaviours. People hiding behind closed doors with anonymity can do whatever they want with a keyboard. Unfortunately, not enough means exist to tackle this issue. Recently the employment of Convolutional Neural Networks and Recurrent Neural Networks are approached for computational purposes for the text classification systems. This work utilizes this for finding foul and malicious comments using the Kaggle data set. The work aims to classify a comment into 6 labels of toxicity. This work also implements a completely functional frontend environment built using React JS and MongoDB, which classifies a user entered text into the mentioned labels of toxicity.
Keywords
Convolutional Neural Network, Toxic Text Classification, Bidirectional LSTM, Pre-trained Word Embeddings, GloVe, Text Classification, Natural Language Processing.
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