5962 Text Analysis for Marketing
Daniel Dan, Ph.D.
Contact details
Weekly hours
Language of instruction
02/21/19 to 02/27/19
Registration via LPIS
Notes to the course
Day Date Time Room
Thursday 03/07/19 05:00 PM - 07:00 PM TC.5.02
Thursday 03/14/19 05:00 PM - 08:00 PM TC.4.13
Thursday 03/21/19 05:00 PM - 08:00 PM TC.5.02
Thursday 03/28/19 05:00 PM - 08:00 PM TC.4.13
Thursday 04/04/19 05:00 PM - 08:00 PM TC.4.13
Thursday 04/11/19 05:00 PM - 08:00 PM TC.4.13
Thursday 05/09/19 05:00 PM - 08:00 PM TC.4.13
Thursday 05/16/19 05:00 PM - 07:30 PM TC.4.13

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.

Learning outcomes
  • Use the R/RStudio environments 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;

  • Understand some popular Machine Learning algorithms applied to Text Analysis;

  • Blend Text Mining and Marketing;

  • Draw conclusions based on the results obtained.

Attendance requirements

Minimum attendance of 80%.

Teaching/learning method(s)

The course is based on interactive lectures, class discussions, individual work, and group work. Classroom discussion is encouraged. Attendance and participation in class as well as interactive 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 over due time will affect the final grading.


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

There will be a total of three assignments to be submitted individually but done in groups. The final 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. 

Prerequisites for participation and waiting lists

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.

Availability of lecturer(s)

Office hours: Fridays 15:00 - 17:00 or by appointment.


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.

Last edited: 2019-02-20