Multimedia Data Mining

Student Projects in Multimedia Data Mining

Contacts (if not stated otherwise): Markus Schedl, Christine Bauer, Navid Rekabsaz

These student projects can be started any time (including holidays) and can span over semester boundaries.

Remark: We are open for new proposals - if you are interested in Multimedia Data Mining, feel free to contact us!



  • Polarity and controversy detection in user-generated texts (Markus Schedl)
  • A comparison of online social networks with respect to connectivity, homophily, and centrality characteristics of users (Markus Schedl)
  • Popularity prediction of movies from their content, i.e., audio, visual, and text (Markus Schedl)
  • Popularity prediction of music tracks from their content, e.g., audio and lyrics (Christine Bauer, Markus Schedl)
  • Cross-dataset comparison of machine learning tasks in music information retrieval (Markus Schedl)
  • Intelligent music browsing interfaces (Markus Schedl)
  • Emotion detection from text (Markus Schedl, Navid Rekabsaz)
  • Personality detection from text (Markus Schedl, Navid Rekabsaz)
  • Music lyrics analysis via linguistic features (Markus Schedl)
  • Spatio-temporal analysis of music taste (e.g., via tweets analysis): comparison across regions, geographic diffusion of music taste over time (Christine Bauer)
  • Characteristics of consumers that consume mainly items that are not considered widely popular or mainstream (Christine Bauer)
  • The role of appearance on a curated playlist for popularity promotion (Christine Bauer)