The program is called EMOTIVE -- an acronym for Extracting the Meaning of Terse Information in a Geo-Visualisation of Emotion. EMOTIVE is a linguistic sentiment analysis tool that scans UK-based Twitter posts at a rate of up to 2,000 Tweets per second. Using a specially developed ontology, EMOTIVE assigns scores to individual Tweets in eight categories, assessing levels of the following emotional states:
In addition to different parts of speech and sentence, EMOTIVE recognizes and understands hashtags and emoticons -- going beyond basic linguistic sentiment analysis.
Of course, accurately detecting and analyzing a person's mood based on their social media posts is nothing new. Researchers have done it many times before, demonstrating that a social media user's content can be used to predict not just a wide variety of emotions, but also facts about the user's identity -- such as gender, location, political affiliation, and other demographic factors.
Linguistic sentiment analysis of Tweets has also been used to accurately predict all sorts of future behavior in other contexts, ranging from movie box office receipts to election outcomes. Most notably (and, perhaps, commonly), it has long been employed to successfully predict stock market behavior.
In a particularly compelling example, computer scientists discovered in 2010 that the level of "calm" detected in Tweets via linguistic sentiment analysis could predict the stock market with 87.6 percent accuracy as many as six days in advance. This research formed the basis of the trading strategy used by Derwent Capital Markets, a hedge fund that invested clients' money solely based on Tweets.
Although Derwent's fund was short-lived and did not yield the 15 percent to 20 percent returns that it boasted that it would, it was still deemed a success. The fund's reported 1.86 percent return outperformed both the overall market and the average hedge fund.
Despite its potentially broad range of applications, EMOTIVE, too, seems to have a specific purpose in mind. Researchers on the EMOTIVE project say that the software will be able to help law enforcement geographically track potential criminal activity and public safety threats. Additionally, they posit that the British government will be able to use EMOTIVE to make policy decisions on national security matters.
how social media played into the equation. Rioters had used Twitter and other social networks to incite, plan, and brag about looting and other illegal activities. (It's possible they even used an app to escape police crowd control tactics.)
Prime Minister David Cameron had even suggested shutting down social media in response to future demonstrations of civic unrest. With EMOTIVE, however, it appears the British government's goal is to work with social media to stay a step ahead of criminals.
It is unclear to what extent each of the eight emotional factors EMOTIVE measures will be helpful in predicting events. The project remains a work in progress. The EMOTIVE team's next step is a prototype that the will purportedly automatically detect events while gleaning even more information from Tweets.
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