Classification Approaches for Social Media Text - Einzelansicht

In this class, we study classification and clustering approaches for social media. Social media data typically comes with text but also other metadata (user information, geo-tags, network structure, etc.) which can be exploited for classification.

We will present different classification algorithms including:

• support vector machines (SVM)
• k-nearest-neighbor
• naive Bayes
• decision trees
• neural network approaches

We will work with concrete implementations of these algorithms to try them out on Twitter data. The class will include a practical component where we collaboratively build a classifier.

Course website: https://www.ling.uni-potsdam.de/~scheffler/teaching/2017classification.html

Final Project: Either a programming project implementing a classification approach based on some social media data, or a theoretical/concept paper discussion different approaches to the same problem, giving a literature overview, etc.