I'm a development team leader with a background in Text Mining, Natural Language Processing and Machine Learning.
My work has focused on applying technologies to autmomatically make sense of information on the Web with applications focused in the Biology and Medical domains.
Currently I work in web development. I lead a team of developers building applicant tracking and people management systems.
Patients are sharing information and their experiences with potentially large audiences all over the world through social media. While sharing in this way may offer immediate benefits to themselves and their readership (e.g. other patients) these unprompted, self-authored accounts of illness are also an important resource for healthcare researchers.
We developed QuTiP – a Text Mining framework which canenable large scale qualitative analyses of patient narratives shared over social media.
Topics: Natural Language Processing, Machine Learning, Health Informatics, Medicine 2.0, Social Media
Using lexical profiling of publicly available web directories, we created a topic-focused Web crawler. This system catalogued web pages related to a specific topic by traversing the Web and assessing relevance according to these lexical topic profiles.
Topics: Term extraction, Web crawlers, Web technologies, terminological modelling
Final Year Project: Topic-focused Web Crawling
Helping companies understand and take control of the traffic heading towards their site with TrafficDefender - an online traffic management and queueing solution.
In addition to building new features for TrafficDefender:
Leading a team of developers working on SaaS recruitment and human resources platform.
As well as improvements and features for the core product, we also managed customisations for a number of clients.
In addition to my software development responsibilities:
Expanding our product’s functionality alongside customising client implementations to meet bespoke business requirements.
Mark Greenwood, Text Mining Patient Experiences from Online Health Communities. PhD Thesis 2015, School of Computer Science & Informatics, Cardiff University, Cardiff, UK. Download
Mark Greenwood, Prioritising Hyperlinks for Topic-Focused Web Crawling using Lexical and Terminological Profiling MPhil Thesis 2009, School of Computer Science, The University of Manchester, Manchester, UK. Download
Irena Spasic, Peter Burnap, Mark Greenwood and Michael Arribas-Ayllon (2012). A naive Bayes approach to topic classification in suicide notes. Biomedical Informatics Insights, Vol. 5, Suppl. 1, pp. 87-97 [PMID: 22879764] [DOI: 10.4137/BII.S8945]
Mark Greenwood, Irena Spasic, Alun Preece, Glyn Elwyn and Nick Francis . (2013). Automatic extraction of personal experiences from patients' blogs: A case study in chronic obstructive pulmonary disease., Proc. of the Third International Conference on Social Computing and its Applications, 2013, Germany. [DOI: 10.1109/CGC.2013.66]
Mark Greenwood and Goran Nenadic (2008). Lexical Profiling of Existing Web Directories to Support Fine-grained Topic-Focused Web Crawling, Proc. of the Corpus Profiling for Information Retrieval and Natural Language Processing Workshop, Oct 2008, London, UK. (pdf)