Social Media (such as Twitter, Facebook, Reddit, YouTube, and so on) is a big part of our daily lives. It provides big unstructured data for researchers to understand individual and societal tendencies. The field of computational linguistics can provide many tools to use this unstructured data and transform it into manageable and scientific or application-oriented research topics.
After a general introduction and a hands-on primer on programming in Python (e.g., with NumPy, sci-kit-learn), we will dive into several exciting topics, including scraping data from social media and text preprocessing, data exploration, and text classification.
The course will be taught in English.
In this seminar, students learn to
• break down a research question/problem into manageable components
• develop an analytical approach to address a research problem in computational sociolinguistics.
• crawl a linguistically valuable social media data
• experiment with data from popular social media platforms
• differentiate various types of social media data and methods to process/analyze them
• apply different classical machine learning or deep learning algorithms in Python Environment to get insights for specific research questions,
• interpret the result of data analysis,
To successfully pass, we ask participants
• to hand in 2-3 small homework assignments
• to present a paper or Python library/package
• to submit a 1-2 page research question/hypothesis summary
Frequency | Weekday | Time | Format / Place | Period |
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Module | Course | Requirements | |
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23-CL-BaCL6 Projektmodul | Projektseminar | Study requirement
Graded examination |
Student information |
23-CL-BaCL6-KF Projektmodul Kernfach | Projektseminar | Study requirement
Graded examination |
Student information |
23-TXT-BaCL6 Projektmodul | Projektseminar | Graded examination
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Student information |
The binding module descriptions contain further information, including specifications on the "types of assignments" students need to complete. In cases where a module description mentions more than one kind of assignment, the respective member of the teaching staff will decide which task(s) they assign the students.