EmotionSensing: predicting mobile user emotions

When:
Friday, March 16, 2018, 10:00 am - 11:00 pm PDTiCal
Where:
6th floor conference room
This event is open to the public.
Type:
AI Seminar - Interview talk
Speaker:
Mahnaz Roshanaei
Video Recording:
https://bluejeans.com/s/KR1hj/
Description:

Mood and emotions of users have a strong impact on their behavior, actions as well as their interactions with other people. They are an important contextual feature for building context-aware pervasive applications. In this research, we investigate new approaches to predict mood and emotional states of individuals and groups of individuals from their online activities in social networks and smartphone usage. In particular, we developed an understanding of the relationship between users’ emotions, their interactions with other people and their activities (personal concerns) and usage in both social networks and smartphones. This work demonstrates the feasibility of using attributes from online social networks and smartphones to predict users’ emotions. The research analyzes two sets of datasets. The first dataset is a large volume of postings from the Twitter social network comprised of profiles and postings of about three million users. The second dataset is collected via a smartphone application used by more than 27 users over a period of more

than two months. This application is specifically developed to gather smartphone activities data of the users, including their current emotions, location, activities, and applications they are running. Based on the analysis of these datasets, we developed highly accurate classifiers to predict user emotions from their online activities in social networks and smartphone usage.

About the Speaker:

I received my Ph.D. in Computer Science in 2016 from the University of Colorado at Boulder. After my Ph.D., I worked as a research scientist at Toshiba Research America Inc for almost a year. I’m currently working as a lecturer at cal state long beach. My research is in applied machine learning, social network and data analysis and computational social science.

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