CANBERRA, May 19 (Xinhua)-- Australian researchers has developed a new online tool called "We Feel" which analyses the words from millions of tweets to display a real-time view of our emotions, a latest research statement from Australia's national science agency, the Commonwealth Scientific and Industrial Research Organization (CSIRO), said Monday.
According to this statement, this tool, which developed for the Black Dog Institute, in partnership with Amazon Web Services, aims to help researchers understand how our emotions fluctuate over time due to changes in social, economic and environmental factors such as weather, time of day, news of a natural disaster or announcements about the economy.
The Black Dog Institute is a world leader in the diagnosis, treatment and prevention of mood disorders such as depression and bipolar disorder.
This tool will help Black Dog Institute's researchers to verify whether the large and fast sample of information coming from Twitter can accurately map our emotions. "It is hoped the tool could help to understand how our collective mood changes, help monitor community mental health and predict where services needed to be assigned,"the researchers from CSIRO said.
Dr Cecile Paris,research leader in language and social computing at CSIRO's Digital Productivity and Services Flagship, confirmed that"We Feel" looks for up to 600 specific words in a stream of around 27 million tweets per day and maps them to a hierarchy of emotions which includes love, joy, surprise, anger, sadness and fear.
"You can explore emotional trends on a minute by minute time scale, across locations around the globe and gender to further refine the results," Cecile Paris added.
And Professor Helen Christensen, executive director of the Black Dog Institute, said the We Feel program represented the world's first foray into understanding how social media can be used to detect poor mental health and observe shifts according to time and place.
"We will have the unique opportunity to monitor the emotional state of people across different geographical areas and ultimately predict when and where potentially life-saving services are required," Professor Helen Christensen explained.