Text Mining

Catalog description

In this course, basic concepts and theory in text mining applications will be presented. Students will learn how natural language processing can collectively capture, classify, and interpret words and their contexts. State-of-the art algorithms and techniques in clustering, classification, anomaly, event detection, and stream analysis for text mining will be presented. Students will learn to investigate linguistic, statistical, and machine learning analysis methods and techniques for modeling information in textual sources. The fundamental theory of text analytics, such as, information retrieval, text extraction, natural language processing, text classification, text clustering, and software algorithms will be addressed. Current research on how text mining techniques are applied in social media platforms and cybercrime prevention will be presented. Advancements in automated text analysis, mining and visualization techniques based on machine learning, knowledge discovery, and natural language processing will be discussed. Information retrieval models and algorithms used in text mining processes and applications will be explored. Students will apply biblical and ethical principles to text mining concepts and processes. Prerequisites: DTAN-500 and STAT-535