New Twitter tool shows a region's emotions in real-time

We Feel passively analyses up to 32,000 tweets per minute. It uses language-processing techniques to look at the words people use in their posts and then maps the words to a 'wheel of emotions' over time.
We Feel passively analyses up to 32,000 tweets per minute. It uses language-processing techniques to look at the words people use in their posts and then maps the words to a 'wheel of emotions' over time. Contributed

CSIRO researchers have unveiled a new online tool which analyses the words from millions of tweets to display a real-time view of our emotions.

Called We Feel, the online tool has been developed for the Black Dog Institute in partnership with Amazon Web Services. 

The tool will help researchers understand how people's 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.

We Feel will help Black Dog Institute researchers verify whether the large and fast sample of information coming from Twitter can accurately map emotions.

It is hoped the tool could help to understand how our collective mood changes, help monitor community mental health and predict where services need to be assigned.

"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," research leader in language and social computing at CSIRO's digital productivity and services flagship, Dr Cecile Paris, said.

"You can explore emotional trends on a minute by minute time scale, across locations around the globe and gender to further refine the results."

The tool has already picked up a spike in the public's emotional response to last week's federal budget, and will continue to collect data that will be analysed by Black Dog's researchers.

We Feel is available to view for a limited time here.


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