Project Summary
Addressing the issue of hate speech is very important in this era where social media platforms are both a tool for connection and a potential ground for discord.
In common language, “hate speech” refers to offensive discourse targeting a group or an individual based on inherent characteristics (such as race, religion or gender) and that may threaten social peace.
To provide a unified framework for the United Nations to address the issue globally, the UN Strategy and Plan of Action on Hate Speech defines hate speech as…“any kind of communication in speech, writing or behaviour, that attacks or uses pejorative or discriminatory language with reference to a person or a group on the basis of who they are, in other words, based on their religion, ethnicity, nationality, race, colour, descent, gender or other identity factor.”
Project Overview: Utilized Python to develop a machine learning model to predict hate speech in text data. The goal is to automatically identify and classify hate speech to improve content moderation and enhance online safety.
- DELIVERABLES
- Utilized Python to develop a cutting-edge machine learning model designed to accurately predict and classify hate speech in textual data.
- Utilized natural language processing (NLP) techniques to preprocess and analyze large datasets, including tokenization, stemming, and lemmatization.
- Incorporated advanced feature extraction methods, including word embeddings and sentiment analysis, to improve classification accuracy and sensitivity.
- Designed and executed extensive data cleaning and augmentation strategies to ensure the robustness and diversity of the training dataset.
- Visualized some common hate and offensive words using WordCloud.
- Applied supervised learning techniques, leveraging labeled datasets to train the model and fine-tune its performance using cross-validation and hyperparameter optimization.
- Developed a scalable model architecture capable of processing and analyzing real-time text data, providing timely detection and response to potential hate speech incidents.
- Evaluated model performance using metrics such as precision, recall and F1-score, achieving high accuracy and reducing false positives/negatives.
- Produced comprehensive documentation and reports detailing the model's development process, performance metrics, and implementation strategies.
- Compared various regression models to arrive at a model with an accuracy of 89.55%.
- ANALYSIS IMPACT
- The hate speech prediction system uses text preprocessing, feature extraction, and various classification algorithms to identify and classify hate speech in text data. By implementing and deploying this model, organizations can enhance online safety and improve content moderation processes.