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Analysis of the nature of loneliness using natural language programming

Our research will investigate loneliness from a large dataset using sentiment analysis, natural language programming and analysis using python to create software that is able to determine the loneliness of an individual. 

Machine learning will be used in order to determine how lonely an individual is through the analysis of open-ended interview questions.

Meet the Principal Investigator(s) for the project

Professor Akram Khan
Professor Akram Khan - Professor Akram Khan is a academic & researcher in the areas of fundamental and applied science. He has published extensively in a wide range of key academic journals. He has worked at most of the leading national laboratories in the world: DESY in Germany, CERN in Switzerland and SLAC in the USA. He read Mathematics and Theoretical Physics for his Bachelors’ degree at St Andrews University, taking his PhD in Experimental Particle Physics at University College London. Akram was a European Research Fellow at CIEMAT in Spain and at CERN in Switzerland, then a Senior Fellow at Edinburgh and Manchester Universities, going on to a faculty position at Stanford University, before joining Brunel University London in 2003. His recent research has been addressing the fundamental questions:'What is the difference between matter and anti-matter?' and 'What new exotic physics processes might help us to address the existing inadequacies of the Standard Model?' As part of his work in the field of applied science he is currently working on developing a novel particle cancer therapy machine in the UK, and on the next generation of internet technologies.'

Related Research Group(s)


Sensors and Instrumentation - Research in detectors, instrumentation, and data analysis methods applied in high energy particle physics, space science, medical imaging, and remote instrumentation and control.

Partnering with confidence

Organisations interested in our research can partner with us with confidence backed by an external and independent benchmark: The Knowledge Exchange Framework. Read more.

Project last modified 13/11/2023