Classification Approaches for Social Media Text - Einzelansicht

Kurs beginnt erst am 20.4.!

Kurssprache: Deutsch und/oder Englisch, wie von Teilnehmer*innen gewünscht

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:

• naive Bayes
• support vector machines (SVM)
• k-nearest-neighbor
• 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.

We will discuss tasks in social media classification such as:

• sentiment
• spam
• impartiality
• hate speech
• bot or not
• on/off topic
• user characteristics: gender, age, occupation, social class
• ... and others

For practical matters, we will also introduce toolkits such as WEKA or RapidMiner if requested by participants, but will mainly use Python and scikit-learn