0013 Online Content Analysis
Daniel Dan, Ph.D.
Contact details
Weekly hours
Language of instruction
09/13/23 to 09/22/23
Registration via LPIS
Notes to the course
Day Date Time Room
Tuesday 10/10/23 05:00 PM - 07:00 PM TC.3.07
Tuesday 10/17/23 05:00 PM - 08:00 PM TC.3.09
Tuesday 10/24/23 05:00 PM - 08:00 PM TC.3.09
Tuesday 11/07/23 05:00 PM - 08:00 PM TC.3.09
Tuesday 11/14/23 05:00 PM - 08:00 PM TC.3.09
Tuesday 12/05/23 05:00 PM - 08:00 PM TC.3.09
Tuesday 12/12/23 05:00 PM - 08:00 PM TC.3.09
Tuesday 12/19/23 05:00 PM - 08:00 PM TC.3.09

The User Generated Content (UGC) 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 enrol 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 environment 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;
  • Do sentiment analysis;
  • Summarize text;
  • Have an good insight on big volumes of text;
  • Understand some popular Machine Learning algorithms applied to Text Analysis;
  • Explore Named-Entity Recognition;
  • Explore Text Classification;
  • Use ChatGPT in R to do Analytics.
  • Blend Text Mining and Marketing;
  • Draw conclusions based on the results obtained.
Attendance requirements

Minimum attendance of 80%. If, due to unforeseen situations, the course is moved online, the attendance rule stays the same. The presence will be assessed by the lecturer at the beginning and at the end of each unit. Extra work must be done in order to compensate for the missing units in agreement with the lecturer.

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%.

The grading scheme is as follows:

< 60%                              fail (5)

60% to 69,99%                sufficient (4)

70% to 79,99%                satisfactory (3)

80% to 89,99%                good (2)

>= 90%                            excellent (1)

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 25.


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Availability of lecturer(s)

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


If course is taught in class.

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 breaks.

Last edited: 2023-04-27