Start date: 1 February 2008
End date: 31 March 2009
Funding programme: e-Infrastructure programme
Project website:
http://www.ncess.ac.uk/research/hub_research/tmfa
JISC theme(s): e-Research
Committees: JISC Support of Research committee
Text-mining technologies offer the opportunity of processing large amounts of textual data systematically, reducing human errors, and saving time. They have the potential to at least partly automate the generation of frames (the process at the heart of frame analysis) to a greater extent than possible using current Computer-Assisted Qualitative Data Analysis Software (CAQDAS) packages. The project will not only produce advanced ICT tools for enabling social scientific research, but also help establish the foundations for the wider adoption and sustainability of NaCTeM text-mining services.
Overview
Frame analysis has been widely adopted for investigating how texts are framed in a certain way to shape the perceptions or opinions of the information’s recipients. Although computer-assisted qualitative data analysis (CAQDAS) packages are available to manage and manipulate textual and/or multimedia data, they are not sufficiently advanced to automate the interpretive work of coding that lies at the heart of frame analysis, nor do they support complex retrievals that need to cope with language variability such as synonymy and polysemy. This project will explore the usefulness of text-mining techniques for the analysis of large media corpora. It builds on the Automatic Summarisation for Systematic Reviews using Text Mining (ASSERT) project.
Aims and objectives
This project, in collaboration with the ESRC Centre for Research on Socio-Cultural Change (CRESC) and National Text Mining Centre (NaCTeM), aims to illustrate how text-mining technologies might advance frame analysis in social science research. The project has two objectives: 1) customising ASSERT’s tools for application to frame analysis of newspaper text; 2) providing a use case to extend awareness and promote adoption of text mining across all social science disciplines.
Project methodology
To achieve the above objectives, the project will:
- investigate frame analysis practices to define initial user requirements for text mining tools
- use an iterative process based on design, rapid prototyping, evaluation and refinement, to customise the ASSERT suite of text mining tools for the qualitative research community
- establish an evaluation framework which may be used in new applications of text mining tools
- investigate potential barriers to adoption, sustainability issues and establish responses such as user training and support
- document the application of text mining tools for media research as an e-framework use case
Anticipated outputs and outcomes
The deliverables of this project are:
Software Outputs
- A frame analysis demonstrator based on a customised version of current ASSERT tools;
Non-Software Outputs
- Documented processes and workflows;
- A case study demonstrating the use of ASSERT text mining tools in social sciences;
- Evaluation framework;
- Use case;
- Barriers report;
- A final evaluation report summarising major findings, lessons learnt and the impact of this project's approach to facilitating effective frame analysis.
Technology / Standards used (if applicable)
In the future, the aim is to integrate these text mining tools with other e-Research tools for linking, processing, managing and sharing multiple forms of social scientific data. There is thus a need for greater coherence in development, and for a map of what has been developed and the standards and specifications that underpin them. This information will enable a strategic approach to planning programmes of development, and would provide institutions with information on what is available and ready for adoption and mainstream use. As such, we will commit to open standards, and encourage interoperability and tool integration.
Lead institution
Project partners