Classifying Disaster-Related Tweets as Real or Fake
- Guided Project
- 2 hours
- Intermediate
- Python
Practice preprocessing text and building deep learning models in TensorFlow to classify disaster tweets and gain valuable NLP skills.
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.
What You'll Learn
- ✓ Explore and Process text data
- ✓ Visualize text data using a word cloud
- ✓ Build text classification model using tokenizer and embedding layer
- ✓ Build sequence models
- ✓ Build a transformer-based text classification model
Before You Start
- ✓ 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
Project Steps
9 steps
- 1 Loading the Data
- 2 Data Exploration
- 3 Text Preprocessing
- 4 Visualization with WordCloud
- 5 Build a Shallow Neural Network
- 6 Build a Multilayer Deep Text Classification Model
- 7 Building a Multilayer Bidirectional LSTM Model
- 8 Building a Transformer Model
- 9 Next Steps
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