Project overview
In this project, you’ll be a data scientist at a news analytics company combating Twitter misinformation during disasters. Using a Kaggle dataset, you’ll build a deep learning model to predict if tweets are about real disasters.
You’ll explore the data, preprocess the text, visualize it with word clouds, and build and evaluate various architectures like shallow nets, multilayer models, LSTMs, and transformers. By the end, you’ll have a robust classifier and hands-on experience with valuable NLP and deep learning skills for real-world applications in data science, social media analytics, and more.
Objective: Use NLP and deep learning in TensorFlow to build a model that identifies real disaster tweets to help fight misinformation spread during emergencies.
Key skill required
To complete this project, it's recommended to build these foundational skills in Python
- Preparing text data for analysis
- Visualizing text data using word clouds
- Building text classification models using tokenizers and embedding layers
- Building sequence models for text classification
Projects steps
Step 1: Loading the Data
Step 2: Data Exploration
Step 3: Text Preprocessing
Step 4: Visualization with WordCloud
Step 5: Build a Shallow Neural Network
Step 6: Build a Multilayer Deep Text Classification Model
Step 7: Building a Multilayer Bidirectional LSTM Model
Step 8: Building a Transformer Model
Step 9: Next Steps
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