0023 Text Analysis for Marketing
Daniel Dan
  • LV-Typ
  • Semesterstunden
  • Unterrichtssprache
11.09.2018 bis 21.09.2018
Anmeldung über LPIS
Hinweise zur LV
Planpunkt(e) Bachelor
Wochentag Datum Uhrzeit Raum
Mittwoch 10.10.2018 16:30 - 18:00 TC.4.14
Mittwoch 17.10.2018 15:00 - 18:00 TC.3.10
Mittwoch 24.10.2018 15:00 - 18:00 TC.3.10
Mittwoch 31.10.2018 15:00 - 18:00 TC.3.10
Mittwoch 07.11.2018 15:00 - 18:00 TC.3.10
Mittwoch 05.12.2018 15:00 - 18:00 TC.3.10
Mittwoch 12.12.2018 15:00 - 18:00 TC.3.10
Mittwoch 19.12.2018 15:00 - 18:00 TC.3.10
Donnerstag 20.12.2018 16:00 - 19:00 TC.4.14

Inhalte der LV

The user generated content on Social Media platforms produces an impressive quantity of information overload.
This induces the need for summarization, discovery of latent dimensions in the text and the necessity to draw conclusions. The course is a hands-on applicative walk-through Text Mining and Analysis, offering tools and solutions applied to Marketing. Students who enroll in this course will learn from basic to advanced techniques of text manipulation. They would also get an insight into information extraction methods and outcome analysis. The ultimate purpose is to find decision making solutions which are useful for consumers and managers alike.

Lernergebnisse (Learning Outcomes)

As a result of this completion of this course the student should be able to:

  •  Use the R/RStudio enviroments in order to apply Text Mining and Analysis;
  • Autonomously gather text information from various sources;
  • Discover latent aspects/dimensions in the text through various techniques;
  • Label the discovered aspects/dimensions;
  • Have an good insight on big volumes of text;
  • Blend Text Mining and Marketing;
  • Draw conclusions based on the results obtained.

Regelung zur Anwesenheit

Students have to be present for at least 75% of the time in class and actively participate to the lecture. Presence under 75% might be admitted only in exceptional cases and could be accepted through the presentation of a strong motivation.


The course is based on interactive lectures, class discussions, individual work, and group work. Classroom discussion is encouraged. Attendance and participation in class discussions are key ingredients to successfully  learn the material of the course and will be part of your grading. Arriving late or turning in assignments late will affect the final grading .

Course materials:

  • The material about the assignments, course slides, datasets and project requirements will be provided via the learn@wu platform in due course. 

Leistung(en) für eine Beurteilung

Grades will be based according to the following criteria:

  • In-class participation, 15%;
  • Assignments, 35%;
  • Workgroup project, 35%;
  • Student presentations, 15%.

There will be a total of four assignments, to be submitted individually. The workgroup project will be an applicative team-work job, based on the assignments. The final presentation will be done by each workgroup member and it will be based on the project. The estimated duration of the presentation is 20 minutes. 

Teilnahmevoraussetzung(en) und Vergabe von Wartelistenplätzen

Some basic R language knowledge. Own laptop computer with R or RStudio installed.

The enrollment in the course is done on a first-come first-served basis. The maximum number of participants is 20.

Erreichbarkeit des/der Vortragenden

Office hours: Thursdays from 15:00  to 17:00.


Electronic Device Policy: Any device admitted if related to the class taught.
Food and Drink Policy: Water and soft drinks are allowed, snacks or food only during the brakes.

Zuletzt bearbeitet: 22.05.2018