Machine Learning Base Spam Comments Detection on YouTube

The project proposes a machine learning-based approach for detecting spam comments on YouTube using Natural Language Processing (NLP) algorithms. As user-generated content continues to grow exponentially, spam comments pose a significant threat to user experience and platform integrity. Leveraging a labeled dataset of comments, the system employs various NLP preprocessing techniques, including tokenization, stop word removal, and stemming, to clean and prepare the text data. Advanced feature extraction methods such as Term Frequency-Inverse Document Frequency (TF-IDF) and word embeddings are used to capture semantic nuances in the comments. Machine learning classifiers like Logistic Regression (LR), Support Vector Machines (SVM), and Random Forest (RF) are then applied to classify comments as spam or legitimate. Accuracy, precision, recall, and F1-score are employed to determine the models in order to ensure a good balance between false positives and negatives. This research enhances content moderation capabilities by filtering spam and providing insights into spam trends, contributing to improved online content management and a better user experience on social media platforms.