EMOTIVE – Mapping Public Sentiment in Tweets

EMOTIVE – Mapping Public Sentiment in Tweets

There are a whole host of social media technologies to aid law enforcement agencies in crime investigation and crime prevention. One specific example from my book, Social Media Under Investigation, Law Enforcement and the Social Web relates to EMOTIVE, a computer programme that analyzes up to 2,000 tweets a second.

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EMOTIVE – Extracting the Meaning Of Terse Information in a Geo-Visualisation of Emotion – allows law enforcement officers identify possible terrorism threats and public disorder offences. The programme was developed by Loughborough University’s Centre for Information Management (CIM) in Leicestershire in the United Kingdom. Developers said it would be possible to use this complex software to monitor the mood of the nation and its reaction to big events – political, cultural, sporting, breaking news or even weather.

Figure 1 – NLP Pipeline for Sparse Text processing (with the EMOTIVE Emotions Ontology Matching Module Highlighted) SOURCE: http://emotive.lboro.ac.uk/

Figure 1 – NLP Pipeline for Sparse Text processing (with the EMOTIVE Emotions Ontology Matching Module Highlighted) SOURCE: http://emotive.lboro.ac.uk/

 

EMOTIVE monitors the traffic of sensitive words and phrases and it can decipher eight key emotions, namely, anger, disgust, fear, happiness, sadness, surprise (Ekman’s 6 basic emotions) + shame, and confusion. In the example below EMOTIVE combines a geo-interface to pin-point the location of the emotionally-charged tweets across England.

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You can see how police forces would use this technology to monitor public sentiment on sensitive or emotionally-charged issues which may present a possible breach of the peace or indeed a more sinister intention such as a terrorism threat. Similarly, it could be used to capture sentiment around less serious but equally important situations such as a public health warning or a severe or unexpected weather event.

The EMOTIVE project is funded by the EPSRC and DSTL and conducted at Loughborough University, within the Centre for Information Management in School of Business and Economics(formerly Information Science department). The researchers working on the project are Prof. Tom Jackson (Principal Investigator), Dr. Ann O’Brien (Co-Investigator), Dr. Martin Sykora (Research Associate), and Dr. Suzanne Elayan (Research Associate).

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