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Cognitive computing approaches for human activity recognition

Cognitive computing approaches for human activity recognition from tweets - A case study of Twitter marketing campaign. Presentation at Rii Forum, April 25, 2019, Rome.

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Cognitive computing approaches for human activity recognition

  1. 1. Cognitive computing approaches for human activity recognition from tweets A case study of Twitter marketing campaign Dr. Jari Jussila & Prashanth Madhala
  2. 2. Applied Data Analytics Workflow
  3. 3. #moodmetricstressinhallintakeino
  4. 4. https://azure.microsoft.com/en-us/services/cognitive-services/computer-vision/#analyze Caption: A woman sitting in a room
  5. 5. Translation of tweet https://translate.google.com/
  6. 6. Sentiment Analysis See e.g. Thelwall (2017)
  7. 7. Activities and Emotions in 2D Valence & Arousal Space
  8. 8. Example of False Positive Anger ”Play Paranoid”
  9. 9. Discussion and conclusions • No libraries or API’s were found that support activity recognition adequately • Combining activity recognition and sentiment analysis provides deeper understanding of customer behavior – which is helpful for marketing • Algorithms provide means for strong (small data) and weak personalization (big data) of stress management service • Current cognitive computing API’s support a limited number of languages - and more importantly are only commodity to users that have programming skills

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