Our team collaborated with the Mood Index of Online Social Indicators team to create a game-changing mood classifier for AI. We developed  metrics to prove mood has social connectivity and can be computed




S1 730

Mood Index of Leading Online Sociability Indicators

Research team

Our team developed metrics for a mood classifier. Our goal was to prove that mood has social connectivity on the internet that could be computed regularly-and hence, at least semi-automatically-and distributed widely. During this doctoral seminar, our team developed metrics for a mood classifier. By collecting vast amounts of Twitter and Facebook feeds, we were successful in classifying several types of moods, like sarcasm and sadness.





In a world increasingly enriched by smart technology, AI technologies are increasingly significant in robotics, automotive, manufacturing, law enforcement, disease and pandemic and care prediction. Machine learning markets are expected to reach $117.19 billion by 2027 (Fortune Business Insights, 2020). What does this mean for the future?


Mood classifiers and deep learning machine learning algorithms will use a mix of text, speech, and facial recognition to identify and anticipate a wide range of emotions using B2B and B2C IoT devices.

In forensics, robotics, automotive government agencies, and law enforcement, text-based mood classifiers that identify a wide variety of emotions, such as those developed by our S1 730 Doctoral Class Team are extremely valuable.

Scientist on Tablet

Large scale social impact.

High financial return. 


Case Studies