One-step and Two-step Classification for Abusive Language Detection on Twitter

• paper published at ALW1: 1st Workshop on Abusive Language Online to be held at the annual meeting of the Association of Computational Linguistics (ACL) 2017 (Vancouver, Canada), August 4th, 2017

Abstract (Park & Fung, 2017)

Automatic abusive language detection is a difficult but important task for online social media. Our research explores a two-step approach of performing classification on abusive language and then classifying into specific types and compares it with one-step approach of doing one multi-class classification for detecting sexist and racist languages. With a public English Twitter corpus of 20 thousand tweets in the type of sexism and racism, our approach shows a promising performance of 0.827 F-measure by using HybridCNN in one-step and 0.824 F-measure by using logistic regression in two-steps.

see whole paper at http://aclweb.org/anthology/W17-3006

Requirements

• python 3.4
• tensorflow 1.0
• keras 2.0 (for tf model layer construction)
• numpy
• sklearn (for splitting dataset)
• nltk (for ngram)
• pandas (for loading csv)
• re (for regex)
• jupyter notebook (for debugging)
• tqdm (for displaying process)
• pickle (for saving python object)
• wordsegment, pyenchant (for preprocessing words)
• gensim (for word2vec)

Scripts

1. train.py

Modules

data

• preprocess.py: clean the tweet
• tokenizer.py: tokenize text into characters/words
• char.py: helpers related to character features
• word.py: helpers related to word features
• hybrid.py: helpers related to char and word together
• utils.py: helpers related to dataset (splitting, batch generation, error analysis)

model

• char_cnn.py: character-level convolutional neural network
• word_cnn.py: word-level convolutional neural network
• hybrid.py: word & char hybrid convolutional neural network

See run results from Tensorboard

• after each run, the logs will be saved at /logs/. check command line log for the exact directory
• use --log_dir option to specify the log folder
• run tensorboard --logdir=/logs/xxxxxx
• if using remote server, ssh with portforwarding -L option. ex. ssh -L 16006:127.0.0.1:6006 jhpark@remoteserver.hk This option will forward remote 6006 port (default port for tensorboard) to localhost 16006.

Notebooks

• unshared_task_analysis/ipynb: analysis of the Hate Speech Dataset
• splitting_dataset.ipynb : scripts for splitting dataset for experiment
• baseline_lr.ipynb : baseline ngram logistic regression experiments

Datasets

1. Hate Speech Dataset: see original/README.md
2. Pretrained word2vec: please download it at https://drive.google.com/file/d/0B7XkCwpI5KDYNlNUTTlSS21pQmM/edit?usp=sharing and put it in data/

Python Style Guide

Please follow PEP 8. https://www.python.org/dev/peps/pep-0008/ https://realpython.com/blog/python/vim-and-python-a-match-made-in-heaven/