Sentiment Analysis with RNNs and LSTMs

Understanding how machines interpret human emotion in text is a cornerstone of modern natural language processing. This text-based course guides you through the foundational concepts of sentiment analysis, starting with key terminology, data preprocessing techniques, and the mechanics of recurrent neural neural networks (RNNs) and Long Short-Term Memory (LSTM) networks. You will discover how to transform raw text into meaningful representations and build models that accurately classify sentiment. What you'll learn: Understand the core architectures of RNNs and LSTMs and how they process sequential text data; Practice preprocessing raw text using tokenization, padding, and word embeddings; Build and train sentiment classification models using modern deep learning frameworks; Evaluate model performance using metrics like accuracy, precision, recall, and F1-score; Apply regularization techniques to prevent overfitting in your text models. The course starts with essential theory and foundational definitions before moving into structured, step-by-step written walkthroughs that show you how to implement and refine your own sentiment analysis models. This course is designed for beginners and aspiring data scientists who want to learn natural language processing without complex prerequisites. Start reading today to build your first text classification model.