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Pfad: VVZ SoSe 2024 > Verzeichnis der LV gegliedert nach Instituten und Abteilungen

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Nr. LV-Typ(en) LV-Titel
5116 PI Online Content Analysis Präsenz-Modus
Anmeldung über LPIS
vom 19.02.2024 15:00 bis 25.02.2024 23:59
Abmeldung über LPIS
vom 19.02.2024 15:00 bis 09.03.2024 23:59

LV-Leiter/in Daniel Dan, Ph.D.
Planpunkte Bachelor SBWL Kurs IV - Digital Marketing
SBWL Kurs IV - Marketing
SBWL Kurs V - Handel und Marketing
SBWL Kurs V - Marketing and Consumer Research
Course IV - Digital Marketing
Course V - Marketing and Consumer Research
Kurs IV - Digital Marketing
Kurs IV - Marketing
Kurs V - Handel und Marketing
Kurs V - Marketing and Consumer Research
Semesterstunden 2
Unterrichtssprache Englisch

Termine
Di, 12.03.2024 17:00-19:00 Uhr D4.0.019 (Lageplan)
Di, 19.03.2024 17:00-20:00 Uhr D4.0.019 (Lageplan)
Di, 23.04.2024 17:00-20:00 Uhr D4.0.127 (Lageplan)
Di, 07.05.2024 17:00-20:00 Uhr D4.0.127 (Lageplan)
Di, 14.05.2024 17:00-20:00 Uhr D4.0.127 (Lageplan)
Di, 21.05.2024 17:00-20:00 Uhr D4.0.136 (Lageplan)
Di, 04.06.2024 17:00-20:00 Uhr D4.0.127 (Lageplan)
Di, 11.06.2024 17:00-20:00 Uhr P D4.0.127 (Lageplan)
Termindownload (ical) | Termine abonnieren

Weitere Informationen https://learn.wu.ac.at/vvz/24s/5116

Kontakt:
daniel.dan@wu.ac.at
Inhalte der LV:

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.

Lernergebnisse (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.
Regelung zur Anwesenheit:

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.

Lehr-/Lerndesign:

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

Leistung(en) für eine Beurteilung:

    • 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)

Teilnahmevoraussetzung(en):

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.

Zuletzt bearbeitet: 04.12.2023 19:19

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