RSS02.E7 Advanced Text Analysis
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.
|26 June 2023 - 30 June 2023|
Early Bird Regular: €895 (application deadline* April 1st)
|Scholarships and discounts||Find more information here|
*Your application is only completed when the course fee has been paid
|Course leader||Petro Tolochko|
|Level of participant||
|Admission requirements||Knowledge of programming is a prerequisite. The class will manly have examples in R programming language, however, if students know other programming languages, they could still follow the course (although they would have to do R-specific research on their own, e.g., for homework).|
|Mode of Study||On Campus|
|ECTS||2 or 4 Find more information here|