Automatic hate speech detection

Setup

1. Install requirements

• python3
• python packages: pandas, sklearn, fasttext, sqlalchemy, ...

2. Configure collector

• Edit hiit_collector.py.example and save it as hiit_collector.py

3. Configure PostgreSQL

• Edit postgre_keys.py.example and save it as postgre_keys.py

4. Get the data

• FastText model for Finnish trained by Facebook using Finnish Wikipedia: Facebook's trained models

Usage:

Collect new data

usage:

`collector.py [-h] [--user USER] [--password PASSWORD] [--hostname HOSTNAME] [--outdir OUTDIR] [--startdate STARTDATE] [--enddate ENDDATE]

optional arguments: -h, --help show this help message and exit --user USER Username --password PASSWORD Password --hostname HOSTNAME Hostname --outdir OUTDIR Directory to store data --startdate STARTDATE Startdate as YYYY-MM-DD --enddate ENDDATE Enddate as YYYY-MM-DD`

Example:

./collector.py --startdate 2017-03-01 --enddate 2017-03-15

Train predictor

Example:

./predict.py --inputdir data/incoming --outdir data/output/ --featurename bow --featurefile data/models/feature_extractor_bow.pkl --predictor data/models/fasttext_svm.pkl

Predict hate speech

Example:

./predict.py --inputdir data/incoming --outdir data/output/ --featurename bow --featurefile data/models/feature_extractor_bow.pkl --predictor data/models/bow_svm.pkl

Sync data

Example:

./sync.py --inputdir data/output/

TODO

2. CNN on Embedding Matrix (c.f Willi)
3. Stemmings, stop words for BoW
4. Study SVM factors (with BoW)
5. Mezadona ? To Models
6. Plot TSNE manifolds for wikipedia model and twitter model

• Highlight hatewords

DONE:

1. Try Naive Bayes-classifier with BoW

• Naive Bayes (Gaussian) did perform comparable to RF, but worse than SVM
• With FastText it performed poorly