Based on Deep Learning Algorithm for Cyberbullying Detection https://pdfs.semanticscholar.org/d581/7c496cf950fd82ef6e05dfa4eaa6f27c24ec.pdf.
Kaggle: https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge/overview
Place train.csv, test.csv and test_labels.csv in cyberbullying-detection/data/interim/
Run the command below to install required libraries:
pip install -r requirements.txt
Download google's pre-trained word embeddings from https://s3.amazonaws.com/dl4j-distribution/GoogleNews-vectors-negative300.bin.gz.
and place it in cyberbullying-detection/data/pretrained_word2vec/ or create own word embedding from the corpus with file src/pre_process/create_embeddings.py.
options = {1: 'corpus', 2: 'google_pre_trained'}
To visualise the embeddings run src/visualisations/word_embeddings.py with the numerical option 1 | 2 as an argument.
Create weights, test and training sequence of the embedding matrix with the program src/pre_process/tokenizer_cnn.py with the specified option. The output will be stored as pickle files.
To run the deep learning model, run src/models/convolutional_neural_network.py, specify the option for the weights to be trained on in the Embedding layer. The program saves the prediction, model as csv and h5 file respectively.
To predict a line of string, run src/models/cnn_predict.py, specify option and the text as input argument. Outputs prediction on the labels.
To get the confusion matrix and classification report run src/models/test_accuracy.py, specify option of the prediction. Output a classification report along with confusion matrix.