200140 SE Advanced Seminar: Mind and Brain (2022W)
Introduction to Computational Social Sciences
Continuous assessment of course work
Labels
Dieses Vertiefungsseminar kann für alle Schwerpunkte absolviert werden.Vertiefungsseminare können nur fürs Pflichtmodul B verwendet werden!
Eine Verwendung fürs Modul A4 Freie Fächer ist nicht möglich.
Eine Verwendung fürs Modul A4 Freie Fächer ist nicht möglich.
Registration/Deregistration
Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).
- Registration is open from Th 01.09.2022 10:00 to Mo 26.09.2022 10:00
- Deregistration possible until Mo 03.10.2022 10:00
Details
max. 20 participants
Language: English
Lecturers
Classes (iCal) - next class is marked with N
Tuesday
04.10.
09:45 - 11:15
PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
Tuesday
11.10.
09:45 - 11:15
PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
Tuesday
18.10.
09:45 - 11:15
PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
Tuesday
25.10.
09:45 - 11:15
PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
Tuesday
08.11.
09:45 - 11:15
PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
Tuesday
15.11.
09:45 - 11:15
PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
Tuesday
22.11.
09:45 - 11:15
PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
Tuesday
29.11.
09:45 - 11:15
PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
Tuesday
06.12.
09:45 - 11:15
PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
Tuesday
13.12.
09:45 - 11:15
PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
Tuesday
10.01.
09:45 - 11:15
PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
Tuesday
17.01.
09:45 - 11:15
PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
Tuesday
24.01.
09:45 - 11:15
PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
Tuesday
31.01.
09:45 - 11:15
PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
Information
Aims, contents and method of the course
In this course students will learn how to use natural language processing tools to automatically analyze the content of large numbers of texts. After this course students will be able to perform sentiment analyses of modern textual sources from the internet but also of historical sources such as fiction work (movies and books) from the past. Students will be able to quantify the temporal change of values and preferences expressed in these textual sources and its relationship to historical events and socio-economic dynamics. BASIC KNOWLEDGE OF PYTHON AND R are highly recommended.
Assessment and permitted materials
20% Class Attendance and Participation
40% Final Project Oral Presentation (Intro and Methods) and Q&A (Individual)
40% Writing up a Project in Article Style (Introduction, Methods, Results and Discussion) (Individual)
40% Final Project Oral Presentation (Intro and Methods) and Q&A (Individual)
40% Writing up a Project in Article Style (Introduction, Methods, Results and Discussion) (Individual)
Minimum requirements and assessment criteria
Students must at least demonstrate conceptual knowledge and understanding of the tools and processes used in computational social sciences. They must be able to conceptualize a project using these tools and present this conceptualization in an oral presentation. Grades improve if students are also able to implement these projects using provided (or custom) scripts in Python and R programming languages, or other computational social sciences tools.
Examination topics
Learning Objectives:
1. Automatically scrape large numbers of texts from the internet
2. Use natural language processing tools to perform automatic text analysis, including sentiment analysis.
3. Quantify the change of values and preferences across time, its relationship with historical events and with socio-economic conditions using basic econometrics analysis tools.Learning activities
1. Lectures: students engage in discussions about the readings and the theory provided by the professors.
2. Practical work: students will aim to design experiments individually (this will also include training students in R and Python).
3. Student Activities (Individual/group): students develop their final projects and work on their assignment.
1. Automatically scrape large numbers of texts from the internet
2. Use natural language processing tools to perform automatic text analysis, including sentiment analysis.
3. Quantify the change of values and preferences across time, its relationship with historical events and with socio-economic conditions using basic econometrics analysis tools.Learning activities
1. Lectures: students engage in discussions about the readings and the theory provided by the professors.
2. Practical work: students will aim to design experiments individually (this will also include training students in R and Python).
3. Student Activities (Individual/group): students develop their final projects and work on their assignment.
Reading list
Martins & Baumard (2022). How to develop reliable instruments to measure the cultural evolution of preferences and feelings in history? Frontiers in Psychology (13) https://osf.io/acukm/
Association in the course directory
Last modified: Mo 25.07.2022 08:28