Building a Sarcasm Detection System with LSTM and GloVe: A Complete Guide
Detecting sarcasm is more than just spotting ironic statements. It involves understanding tone, context, and sometimes even cultural nuances. Sarcasm can be difficult for machines to detect in social media posts, news headlines, or everyday conversations because it contradicts the literal meaning of words. Yet, modern NLP techniques can pick up on these subtleties better than ever with the right approach and data preprocessing.
Below, you’ll find a detailed, step-by-step guide on how to build your sarcasm detection model using LSTM (Long Short-Term Memory) networks and GloVe embeddings. From data cleaning and preprocessing to model deployment in a Streamlit application, this post covers every element you need to create a robust sarcasm detection system.
Sarcasm detection is a fascinating natural language processing (NLP) challenge. Sarcastic statements often convey the opposite of their literal meaning, making them tricky for machines to identify. For instance, the sentence “I love getting stuck in traffic for hours” may say you enjoy traffic, but in reality, you mean the opposite. Automated sarcasm detection requires models that can glean subtle contextual cues. In this post, we’ll train an LSTM model on a sarcasm headlines dataset and deploy it using Streamlit to create a friendly, interactive web interface.
