Sarcasm Detection with Deep Learning
Built a deep learning model for detecting sarcasm in text headlines using a public dataset — workflow and modeling inspired by the Scalar master class
Sarcasm Detection Headlines Classifier
Trained a deep learning model to classify news headlines as sarcastic or not. The workflow, modeling steps, and interpretation functions are based on Scalar master class tutorials, but the actual data is a public JSON file of labeled headlines.
The project loads news headline data labeled for sarcasm, preprocesses text using Keras Tokenizer and padding, and trains a Sequential model. Main layers: Embedding, GlobalAveragePooling1D, Dense.
Sarcasm detection is inherently difficult due to context and linguistic subtleties. Achieving robust model accuracy with simple architecture is tough for nuanced text like humor or sarcasm.
Used a Keras Sequential model (Embedding, pooling, dense). Accuracy: ~97% train, ~83% test. Original Scalar master class code structure enhanced for interpretability; handles custom predictions and output transformation.
Key Features
- Headline-based sarcasm detection
- Tokenization & Padding
- Embedding & Pooling layers
- Scalar master class code reference
- Custom output interpretation
- Keras/TensorFlow workflow