Text and data mining

Data mining is an analytic process of exploring large data sets with the aim of discovering consistent patterns and systematic relationships between relevant variables.

In particular, text mining relies on natural language processing to formally structure and quickly interpret text data prior to their mining. The ultimate goal of data mining is to derive information and knowledge from the data in order to help users make intelligent decisions about complex problems. For example, businesses use text and data mining to improve competitiveness based on the analysis of customer and competitor data.

In this priority area, we are especially interested in applying text and data mining in the areas of:

healthcare
policing
life sciences
social sciences.

Priority area leader

Professor Irena Spasic

Professor

Related projects

Title	Holder	Source	Value (£Ks)
Website Yourplacenames.com, also known as People’s Place Names	Prof C Jones	Ordnance Survey Ltd	12.85
Using qualitative analysis of patient blogs to inform development of automated measurement of self-care with text mining and sentiment analysis	Dr K Button, Dr I Spasic and Professor A Smith	Wellcome Trust	22.33
Improving the customer experience in retail: bringing big data to small users	Dr C Mumford	Technology Strategy Board	119.81
Corpws Cenedlaethol Cymraeg Cyfoes (The National Corpus of Contemporary Welsh): A community driven approach to linguistic corpus construction	Dr D Knight, Prof T Fitzpatrick, Dr J Evas and Dr I Spasic	ESRC	1829.88
Improving the consequence management of security events: A simulation exercise for policy and practice development	Professor A Preece, Dr I Spasic and Professor M Innes (Social Science)	ESRC	14.15
Implementation of TRAK to develop eRehab for knee conditions: A web based application suite to support self-management in rehabilitation	Dr K Button, Prof R Van-Deursen and Dr I Spasic	Health Foundation via Cardiff & Vale UHB	55.04
To investigate how a computer based collaborative filtering system can be developed to offer self-serve investment options for retail investors	Dr J Shao	KTP & Equiniti & Welsh Government	299.68
Field experiment using the 'Sentinel' social media analytics tool developed by Cardiff University to inform the police and partner response to events surrounding Halloween and Bonfire Night in South Wales	Dr S. Tucker, Professor M. Innes (Social Science) and Professor A. Preece	South Wales Police	17.2
Soft Facts: Social medial and spontaneous community mobilisation: The role of rumours after major crime events	Professor M. Innes, Professor A Preece and Dr I. Spasic	Nesta	10.0
After Woolwich: Social reactions on social media	Professor M. Innes, Professor A Preece and Dr I. Spasic	ESRC	182.63