Week 6-BALT 4364-Chapter 5

Chapter 5 introduces Natural Language Processing (NLP), a major branch of artificial intelligence focused on enabling computers to understand, generate, and work with human language. It explains key NLP tasks such as text classification, sentiment analysis, and language modeling, showing how machines convert text into structured representations they can learn from. The chapter also explores practical applications like spam filtering, news categorization, and customer feedback analysis. By learning how NLP works, students gain insight into how modern AI systems interpret massive amounts of text.

The chapter describes text classification as the process of assigning categories to text, which is essential for tasks like spam detection and topic labeling. Sentiment analysis is covered next, focusing on detecting emotions or opinions in reviews, surveys, or social media posts. Examples show how companies use sentiment analysis to measure customer satisfaction or public reaction to movies, products, and events. Finally, language modeling is explained as predicting the next word in a sequence, forming the foundation of systems like speech recognition and text generation.

                                             

The chapter also includes multiple hands-on coding exercises that guide students through building NLP models using Python, TensorFlow, and real datasets. Students practice tasks such as classifying news articles, detecting sentiment in movie reviews, and generating headlines using LSTMs and embeddings. Each exercise demonstrates critical preprocessing steps like tokenization, padding, and creating word embeddings. These practical examples help students understand not only the theory but also the technical workflow behind building NLP models.

Overall, this chapter provides a strong and accessible introduction to NLP, blending both conceptual knowledge and real-world coding experience. The examples are practical and show clearly how businesses rely on NLP for automation, customer insight, and decision-making. It also highlights how language models like the one powering ChatGPT use patterns in text to generate predictions, making the chapter feel relevant and modern. The exercises are especially useful for beginners because they break down complex workflows into manageable steps and encourage students to experiment with real data.

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