Hate Speech Detection using Deep Learning

Overview

This project aims to build a deep learning-based system to automatically detect hate speech in social media text, specifically tweets. The model leverages Natural Language Processing (NLP) techniques for text preprocessing and employs a neural network model using Keras and TensorFlow for classification.

Objectives

• Detect and classify tweets as hate speech, offensive language, or neither.
• Apply NLP techniques to clean and preprocess text data.
• Build and train a neural network model to achieve reliable prediction performance.

Dataset

The dataset used in this project consists of labeled tweets with the following classes:

• 0 - Hate Speech
• 1 - Offensive Language
• 2 - Neither

The dataset was sourced from a CSV file and contains approximately 24,000 entries.

Project Structure

1. Data Loading and Exploration
   Load the dataset and examine basic statistics and class distributions.

2. Text Preprocessing
   Clean the text by removing noise, stopwords, and applying lemmatization using NLTK.

3. Tokenization and Padding
   Convert text into numerical sequences and pad them to ensure uniform input length.

4. Model Building
   Construct a deep learning model using Keras Sequential API. Common layers include:

   • Embedding Layer
   • Bidirectional LSTM
   • Dense Layers

5. Training and Evaluation
   Train the model on the preprocessed data and evaluate its performance using accuracy and loss metrics.

6. Visualization
   Plot accuracy and loss curves to monitor model performance over epochs.

Dependencies

• Python
• NumPy
• Pandas
• Matplotlib / Seaborn
• NLTK
• TensorFlow / Keras
• WordCloud
• scikit-learn