13 citations · 26 across the 10 of their papers we have counts for
13 papers
Best Preprocessing Techniques for Sentiment Analysis
Saranzaya Magsarjav, Melissa Humphries, Jonathan Tuke +1
Sentiment analysis in Twitter datasets is important because it enables monitoring public opinion on products and analysis of political and social movements. One critical step is pr…
Probabilistic emotion and sentiment modelling of patient-reported experiences
Curtis Murray, Lewis Mitchell, Jonathan Tuke +1
This study introduces a novel methodology for modelling patient emotions from online patient experience narratives. We employed metadata network topic modelling to analyse patient-…
Revealing Patient-Reported Experiences in Healthcare from Social Media using the DAPMAV Framework
Curtis Murray, Lewis Mitchell, Jonathan Tuke +1
Understanding patient experience in healthcare is increasingly important and desired by medical professionals in a patient-centered care approach. Healthcare discourse on social me…
Are we always in strife? A longitudinal study of the echo chamber effect in the Australian Twittersphere
Mehwish Nasim, Derek Weber, Tobin South +4
Contrary to expectations that the increased connectivity offered by the internet and particularly Online Social Networks (OSNs) would result in broad consensus on contentious issue…
Symptom extraction from the narratives of personal experiences with COVID-19 on Reddit
Curtis Murray, Lewis Mitchell, Jonathan Tuke +1
Social media discussion of COVID-19 provides a rich source of information into how the virus affects people's lives that is qualitatively different from traditional public health d…
A framework for streamlined statistical prediction using topic models
Vanessa Glenny, Jonathan Tuke, Nigel Bean +1
In the Humanities and Social Sciences, there is increasing interest in approaches to information extraction, prediction, intelligent linkage, and dimension reduction applicable to…