<?xml version="1.0" encoding="UTF-8"?><ns2:project xmlns:ns1="http://gtr.rcuk.ac.uk/gtr/api" xmlns:ns2="http://gtr.rcuk.ac.uk/gtr/api/project" xmlns:ns3="http://gtr.rcuk.ac.uk/gtr/api/fund" xmlns:ns4="http://gtr.rcuk.ac.uk/gtr/api/person" xmlns:ns5="http://gtr.rcuk.ac.uk/gtr/api/project/outcome" xmlns:ns6="http://gtr.rcuk.ac.uk/gtr/api/organisation" ns1:created="2026-06-03T15:52:43Z" ns1:href="http://gtr.ukri.org/gtr/api/projects/D60A871B-B365-4ED6-BC4F-7490FEFE5AD0" ns1:id="D60A871B-B365-4ED6-BC4F-7490FEFE5AD0"><ns1:links><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/persons/CCC6854F-DF9A-4B78-8848-37DC5BCE8786" ns1:rel="PM_PER"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/3CEDB4B8-B850-4AB5-A682-AAF113B1E83D" ns1:rel="LEAD_ORG"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/3CEDB4B8-B850-4AB5-A682-AAF113B1E83D" ns1:rel="PARTICIPANT_ORG"/><ns1:link ns1:end="2016-05-30T23:00:00Z" ns1:href="http://gtr.ukri.org/gtr/api/funds/9A912565-B3C5-4BF1-AD81-8375268553D4" ns1:rel="FUND" ns1:start="2015-03-31T23:00:00Z"/></ns1:links><ns2:identifiers><ns2:identifier ns2:type="RCUK">710670</ns2:identifier></ns2:identifiers><ns2:title>Emotion Detection for Social Media Applications (EDeSMA)</ns2:title><ns2:status>Closed</ns2:status><ns2:grantCategory>GRD Proof of Concept</ns2:grantCategory><ns2:leadFunder>Innovate UK</ns2:leadFunder><ns2:abstractText>Automatic emotion detection in short text posts represents an exciting avenue for R&amp;amp;D at the
cross-section of text/data mining in social media. Considering that social media has no
geographic or time boundaries, people are posting comments from everywhere and while in
different emotional states. Therefore, organisations are interested to know the emotions of
their clients about their own brands and those of competitors. This project is about
establishing the potential benefits of augmenting listening247, the main software as a service
platform of DigitalMR, with emotion detection capabilities. The R&amp;amp;D tasks include machine
learning modelling of psycholinguistic phenomena. The project will produce dedicated large
datasets for training and testing. The DigitalMR team will benefit from regular input by a
scientific panel with experts in psychology and machine learning from UK Universities as
well as user feedback from three corporate challenge partners.</ns2:abstractText></ns2:project>