Text Mining

Objectives

The Text Mining curricular unit aims to enable students to acquire a fundamental understanding of the science of Text Mining in supervised and unsupervised problems, easily transferable to various real-world challenges in Data Science.

This course will cover different topics and challenges commonly associated with processing and manipulating text data in Data Science projects, presenting the essential methodological aspects of Text Mining and the most important and currently utilized tools, focusing on traditional Machine Learning algorithms.

The topics to be addressed include predictive and descriptive algorithms in different contexts, such as the application of Naïve Bayes and K-Means in problems like sentiment analysis and document clustering.

By the end of the course, students will be able to utilize the acquired skills to produce a fully processed dataset compatible for the application of machine learning models targeted towards text data, enabling the extraction of relevant knowledge for decision-making across various contexts.