suman101112 / online-hate-speech-recog

An online hate speech recognition system.

An online Multilingual Hate Speech Detection System

Setting up Requirements

To get started with the project, follow the below instructions

virtualenv -p python3 py3tf
source py3tf2/bin/activate

pip install git+https://github.com/neerajvashistha/indic_nlp_library
pip install git+https://github.com/neerajvashistha/ekphrasis

pip install tensorflow-gpu==1.13.1
pip install torch==1.3.1+cpu -f https://download.pytorch.org/whl/torch_stable.html
pip install inltk

git clone https://github.com/neerajvashistha/online-hate-speech-recog.git

cd online-hate-speech-recog

pip install -r requirements.txt

Onces all the requirements are installed. Add module data and models to the project. Run the below command in online-hate-speech-recog directory in py3tf virtual environment .

python setup.py

Project Structure

The project structure is defined as

-> online-hate-speech-recog/
	-> data/
		-> en/
			-> hasoc2019/format_data.ipynb
			-> hate-speech-offensive-language/format_data.ipynb
			-> hate_speech_icwsm18/format_data.ipynb
			-> ousidhoum-etal-multilingual-hate-speech-2019/format_data.ipynb
			-> semeval2018/format_data.ipynb
		-> hi/
			-> hasoc2019/format_data.ipynb
			-> Hinglish-Offensive-Text-Classification/
				-> Hinglish_Profanity_List.csv
				-> format_data.ipynb
		-> dataset_en.csv
		-> dataset_hi.csv
		-> dataset_hi_cdmx.csv
	-> model/
		-> hi-sentiment/
	-> src/
		-> utils/
		-> static/
		-> templates/
		-> app.py
		-> feature_generation.py
		-> base_line_LR.py
		-> base_line_DNN.py
		-> base_line_model.ipynb
		-> base_line_CNN_LSTM.ipynb
		-> en_Bert_Based_Model.ipynb
		-> hi_Bert_Based_Model.ipynb
		-> hi_cdmx_Bert_Based_Model.ipynb
	-> setup.py
	-> requirements.txt
	-> README.md

All the important files are mentioned above. We describe the information about each file below.

• The format_data.ipynb files present in data/en and data/hi are responsible for converting original categories into homogeneous set of classes of Hate, Abusive or neither. They are also responsible for curating some of the text from Twitter API.
• Hinglish_Profanity_List.csv was originally created by P Mathur et.al is now updated with more words and scripted devanagari hindi words.
• model/hi-sentiment this directory contains our implementation of Sentiment Analysis in Hindi language using transfer learning technique, built in Fastai. model directory also contains, BERT CNN_LSTM and Logistic regression models.
• src/utils utility functions and modules on hindi tokentisation and cleaning 'byte encoded' emoji from text.
• static, templates and app.py are responsible for online application, providing web interface for live chat room environment, utilises models and generates new_train.csv
• The feature_generation.py is the main feature generation process used for pre-processing english and hindi tweets both for Logistic regression and CNN LSTM models. It also serves as the file for generating feature vetor (TFIDF and POS vectors) for Logistic regression
• base_line_LR.py file contains Logistic regression model, with functionality to train, test, predict and generate classification and confusion matrix
• base_line_DNN.py, this file contains ProcessData class and CNN_LSTM class. The ProcessData class is reposibile of converting tweet samples into word embedding sequences. The CNN_LSTM class contains the network and training and prediction functionality modules.
• base_line_model.ipynb illustrates the logistic regression model while base_line_CNN_LSTM.ipynb illustrates the CNN LSTM model. In order to run base_line_CNN_LSTM.ipynb we require a GPU.
• en_Bert_Based_Model.ipynb, hi_Bert_Based_Model.ipynb and hi_cdmx_Bert_Based_Model.ipynb contains the code for running BERT based model. This has been extensively tested on Google Colab TPU.

Executing Models and evaluating Performace

In order to run the models, execute,

(py3tf)$ cd online-hate-speech-recog/src
(py3tf)$ jupyter notebook

• base_line_model.ipynb requires CPU
• base_line_CNN_LSTM.ipynb requires GPU and
• xxx_Bert_Based_Model.ipynb requires TPU.

In order to run the online application, please make sure, port 8893 is open.

(py3tf)$ cd online-hate-speech-recog/src
(py3tf)$ python app.py