Natural Language Processing with Disaster Tweets

The Disaster Tweets challenge is a beginner-friendly NLP competition featured in the 2024 Machine Learning Marathon (MLM24). Participants build models to classify whether tweets are about real disasters or not – an accessible introduction to natural language processing and text classification.

Challenge design

• Task: Predict whether a given tweet is about a real disaster (1) or not (0).
• Domain: Natural language processing – text classification with real-world social media data.
• Methods: Approaches range from TF-IDF + logistic regression to fine-tuned transformer models (BERT, DistilBERT).

Links

• Kaggle challenge: Natural Language Processing with Disaster Tweets

Related resources

• Workshop: Intro to Natural Language Processing (NLP): Brush up on NLP basics before tackling text classification challenges.
• Library: Hugging Face: Fine-tuning transformer models like BERT and DistilBERT is a top approach — learn how to find and use them on Hugging Face.
• Workshop: Intro to Deep Learning with PyTorch: Learn deep learning fundamentals for building text classification models beyond TF-IDF baselines.

Questions

If you have any lingering questions about this project, please feel free to post to the Nexus Q&A on GitHub. We will improve materials on this website as additional questions come in.