AI-Driven Text Analysis for Business.

Catalog description

Semester course; 3 lecture hours (delivered hybrid). 3 credits. Prerequisite: INFO 350 or INFO 648 . Teaches AI-driven methods and techniques used to discover interesting patterns and extract valuable information from textual data to support decision making. Students will learn the fundamentals of text analytics using both traditional machine learning and advanced deep learning methods. Topics include text representation (e.g., TF-IDF, embeddings), topic modeling and sequence modeling with recurrent neural networks and long short-term memory networks. The course also explores transformer-based large language models and their applications in business contexts such as sentiment analysis, text classification and agentic systems.