RSS02.E7 Advanced Text Analysis - Confirmed

Confirmed

Classical content analysis used to be an expensive (monetarily and computationally) method. Nevertheless, with the constant technological advancement, the availability of data is at an all-time high, and the methods to analyze the data are constantly increasing in potential. At its core, text-as-data approaches have the same aim as classical content analysis – extracting meaning out of text. However, due to the unstructured and multidimensional nature of texts, there are additional challenges in achieving this goal.

This course is aimed at people who have some experience with text-as-data approaches, but what to understand more nuanced aspects of methods used to analyze texts. The topics that are covered in the course are: Text Representation (ways to transform unstructured text for computational analyses), Statistical models of texts, Word Embedding Models, Deep Learning models applied to text analysis, and Multilingual text analysis. The advantages and disadvantages of each method/approach are also discussed.

The topics are introduced in an interactive lecture-type setting, while the practical part consists of a coding session with examples and tasks. Small homework assignments are given out throughout the course week to deepen the knowledge of the topics.