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Multilabel Classification of Tagalog Hate Speech using Bidirectional Encoder Representations from Transformers (BERT)

Important

A rework of this project's JS-based web app (v2) has been moved to syke9p3/retrain-mlthsc (07-25-2024)

Reworked (again!) in React + Vite + Typescript (v3) syke9p3/mlthsc-react (08-27-2024)

📋 About the Project

This repository contains source files for the thesis entitled Multilabel Classification of Tagalog Hate Speech using Bidirectional Encoder Representations from Transformers (BERT). The model classifies a hate speech according to one or more categories: Age, Gender, Physical, Race, Religion, and Others.

Hate speech encompasses expressions and behaviors that promote hatred, discrimination, prejudice, or violence against individuals or groups based on specific attributes, with consequences ranging from physical harm to psychological distress, making it a critical issue in today's society.

Bidirectional Encoder Representations from Transformers (BERT) is pre-trained deep learning model used in this study that uses a transformer architecture to generate word embeddings, capturing both left and right context information, and can be fine-tuned for various natural language processing tasks. For this project, we fine-tuned Jiang et. al.'s pre-trained BERT Tagalog Base Uncased model in the task of multilabel hate speech classification.

👥 Proponents

📄 Abstract

Hate speech promotes hatred, discrimination, prejudice, or violence against individuals or groups based on specific attributes, leading to physical and psychological harm. This study addresses the prevalence of hate speech on social media by proposing a Tagalog hate speech multilabel classification model. Using a fine-tuned Bidirectional Encoder Representations from Transformers (BERT) model, the study classifies hate speech into categories such as Age, Gender, Physical, Race, Religion, and Others. Analyzing 2,116 manually annotated social media posts from Facebook, Reddit, and Twitter, the model achieved varying precision, recall, and f-measure scores across categories, with an overall hamming loss of 3.79%.

🔠 Keywords

Bidirectional Encoder Representations from Transformers; Hate Speech; Multilabel Classification; Social Media; Tagalog; Polytechnic University of the Philippines; Bachelor of Science in Computer Science

💻 Languages and Technologies

Model

Python PyTorch Jupyter Notebook Hugging Face Pandas Numpy Numpy

User Interface

HTML5 CSS3 JavaScript Flask

🖼 Screenshots

🎨 Labels

Multilabel Classification refers to the task of assigning one or more relevant labels to each text. Each text can be associated with multiple categories simultaneously, such as Age, Gender, Physical, Race, Religion, or Others.

Label Description
Age Target of hate speech pertains to one's age bracket or demographic
Gender Target of hate speech pertains to gender identity, sex, or sexual orientation
Physical Target of hate speech pertains to physical attributes or disability
Race Target of hate speech pertains to racial background, ethnicity, or nationality
Religion Target of hate speech pertains to affiliation, belief, and faith to any of the existing religious or non-religious groups
Others Target of hate speech pertains other topic that is not relevant as Age, Gender, Physical, Race, or Religion

📜 Dataset

2,116 scraped social media posts from Facebook, Reddit, and Twitter manually annotated for determining labels for each data split into three sets:

Dataset Number of Posts Percentage
Training Set 1,267 60%
Validation Set 212 10%
Testing Set 633 30%

Training

The BERT Tagalog POS Tagger were trained using PyTorch library with the following hyperparameters set:

Hyperparamter Value
Batch Size 8
Training Epoch 5
Learning-rate 2e-5
Optimizer Adam

🔢 Results

The testing set containing 633 annotated hate speech data used to analyze performance of the model in its ability to classify the hate speech input according to different label in terms of Precision, Recall, F-Measure, and overall hamming loss.

Label Precision Recall F-Measure
Age 97.12% 90.18% 93.52%
Gender 93.23% 94.66% 93.94%
Physical 92.23% 71.43% 80.51%
Race 90.99% 88.60% 89.78%
Religion 99.03% 94.44% 96.68%
Others 83.74% 85.12% 84.43%

Overall Hamming Loss: 3.79%

🛠️ Installation

📦 Clone with git-lfs

Since this repo contains large data files (>= 50MB), you need to first download and install a git plugin called git-lfs for versioning large files, and set up Git LFS using command git lfs install in console, in order to fully clone this repo.

🏃 How to run

Setup model

  • Clone the repository:
git clone https://github.com/kenth9p3/mlthsc-thesis.git
  • Create a virtual environment:
# Windows
python -m venv venv

# Linux
python3 -m venv venv
  • Activate virtual environment:
# Windows
source venv/Scripts/activate

# Linux
source venv/bin/activate
  • Install dependencies:
pip install -r requirements.txt
  • Run app:
python ./server.py

Setup user interface

  • Run index.html in the browser

  • Input Tagalog hate speech in text box or choose one of the examples

  • Click Analyze

  • Save results