Syllabus

Title
0023 Text Analysis for Marketing
Instructors
Daniel Dan
Contact details
  • Type
    PI
  • Weekly hours
    2
  • Language of instruction
    Englisch
Registration
09/11/18 to 09/21/18
Registration via LPIS
Notes to the course
Subject(s) Bachelor Programs
Dates
Day Date Time Room
Wednesday 10/10/18 04:30 PM - 06:00 PM TC.4.14
Wednesday 10/17/18 03:00 PM - 06:00 PM TC.3.10
Wednesday 10/24/18 03:00 PM - 06:00 PM TC.3.10
Wednesday 10/31/18 03:00 PM - 06:00 PM TC.3.10
Wednesday 11/07/18 03:00 PM - 06:00 PM TC.3.10
Wednesday 12/05/18 03:00 PM - 06:00 PM TC.3.10
Wednesday 12/12/18 03:00 PM - 06:00 PM TC.3.10
Wednesday 12/19/18 03:00 PM - 06:00 PM TC.3.10
Thursday 12/20/18 04:00 PM - 07:00 PM TC.4.14

Contents

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

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.

Attendance requirements

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.

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 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. 

Assessment

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. 

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: Thursdays from 15:00  to 17:00.

Other

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: 2018-05-22



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