Identifying Hate Speech on Online Platforms in Bangladesh Using Machine Learning - Sentiment Analysis

This project aims to address the rising concern of hate speech on digital platforms in Bangladesh by developing a machine learning-based detection system. With a focus on Bengali language text, this project utilizes Natural Language Processing (NLP) techniques and a custom-curated dataset that reflects the unique linguistic and cultural characteristics of Bangladesh.

Project Objectives

1. Hate Speech Detection: Build an effective model to identify offensive and harmful language in Bengali text on online platforms.
2. Content Moderation Support: Provide a reliable tool to assist in moderating content, ensuring safer and more inclusive digital interactions.
3. Promote Responsible Online Discourse: Contribute to a healthier digital environment in Bangladesh by facilitating the automated detection of hate speech.

Methodology

The project workflow includes the following steps:

• Data Preprocessing: Clean and prepare the Bengali text data to ensure accuracy and relevance.
• Feature Extraction: Apply NLP techniques to extract meaningful features for effective model training.
• Algorithm Evaluation: Implement and assess various machine learning algorithms, such as Support Vector Machine (SVM), Logistic Regression, Decision Trees, and more, to determine the most effective model for hate speech detection in Bengali.

Project Impact

By developing this system, we aim to provide a valuable tool for content moderation on social media and online platforms, enhancing the ability to detect hate speech quickly and accurately. This project supports the creation of safer online communities and encourages responsible digital engagement across Bangladesh.