<p>Lecture, three hours; discussion, one hour. Limited to Statistics and Data Science Department students. Exposure to basic concepts of text data and text data analysis. Hands-on study covering use of text mining tools for purpose of data analysis. Covers basic text handling, natural language processing, clustering, classification, and statistical modeling applied on textual data. P/NP or letter grading.</p>

Statistics

College of Letters and Science

Container 1

Statistics and Data Science

STATS 133 - Introduction to Text Mining Using R

Introduction to Text Mining Using R

Learn about key computer science methods for text mining, such as text classification and opinion mining.

To describe basic concepts and methods in text mining, for example document representation, information extraction, text classification and clustering, and topic modeling;

To use benchmark corpora, commercial and open-source text analysis and visualization tools to explore interesting patterns

To enable businesses to automatically identify useful information in emails, social media posts, customer service tickets, chatbots and other text.

Understand how to approach narrative analysis, thematic analysis, and metaphor analysis.

Learn the foundations of Natural Language Processing (NLP).

Learn how text mining tools have been used successfully by social scientists.

To develop skills for using recent data mining software (example R) to solve practical problems in a variety of disciplines.

Understand basic text processing techniques.

To uncover patterns and relationships in unstructured text.

To gain experience doing independent study and research using unstructured data like text.

General Catalog

Introduction to Text Mining Using R

STATS 133

4 units