The document discusses a study on cognitive computing techniques for recognizing human activities from tweets, particularly in the context of a Twitter marketing campaign. It concludes that existing libraries and APIs for activity recognition are insufficient, but integrating this with sentiment analysis enhances understanding of consumer behavior. The study highlights the need for more accessible cognitive computing APIs that cater to a wider audience beyond those with programming skills.

1. Cognitive computing approaches
for human activity recognition
from tweets
A case study of Twitter marketing campaign
Dr. Jari Jussila & Prashanth Madhala

4. Applied Data Analytics Workflow

5. #moodmetricstressinhallintakeino

7. https://azure.microsoft.com/en-us/services/cognitive-services/computer-vision/#analyze
Caption: A woman sitting in a room

8. Translation of tweet
https://translate.google.com/

9. Sentiment Analysis
See e.g. Thelwall (2017)

10. Activities and Emotions in 2D Valence
& Arousal Space

11. Example of False Positive Anger
”Play Paranoid”

12. 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