Manual and automated Annotation

Computational Text Analysis describes the use of computer-based methods to analyze large collections of textual data. As computational text analysis is heterogeneous in nature, strong foundational knowledge is necessary to address recurring methodological challenges. This practical course begins with an introduction to the fundamental concepts of Computational Text Analysis. We will further examine the creation and use of textual corpora, along with a selection of widely applied methods (namely analysing lexical fields, sentiment analysis, distributional semantics, and machine learning). We will close by putting the presentation and assessment of research into practice. Students are encouraged to include their own research interests and are expected to independently execute scientific research. Regular attendance is essential for keeping up with the course. There will be weekly exercises. Programming skills are not required. Requirements for a successful completion of the course: - Submission of an abstract - Peer feedback for two other student projects - A short term paper