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:
- life sciences
- social sciences.
Priority area leader
Director of Research and Deputy Head of School
|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|