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Found 8 papers, 2 papers with code
Life still goes on: Analysing Australian WW1 Diaries through Distant Reading
This gives quantitative researchers an opportunity to use distant reading techniques, as opposed to traditional close reading, in order to analyse larger quantities of historic data.
Probabilistic emotion and sentiment modelling of patient-reported experiences
This study introduces a novel methodology for modelling patient emotions from online patient experience narratives.
Personality Profiling: How informative are social media profiles in predicting personal information?
While we apply these methods to Myers-Briggs personality profiling, they could be more generally used for any labelling of individuals on social media.
Revealing Patient-Reported Experiences in Healthcare from Social Media using the DAPMAV Framework
In this paper, we introduce the Design-Acquire-Process-Model-Analyse-Visualise (DAPMAV) framework to provide an overview of techniques and an approach to capture patient-reported experiences from social media data.
Symptom extraction from the narratives of personal experiences with COVID-19 on Reddit
In particular, when individuals self-report their experiences over the course of the virus on social media, it can allow for identification of the emotions each stage of symptoms engenders in the patient.
Podlab at SemEval-2019 Task 3: The Importance of Being Shallow
This paper describes our linear SVM system for emotion classification from conversational dialogue, entered in SemEval2019 Task 3.
A framework for streamlined statistical prediction using topic models
In the Humanities and Social Sciences, there is increasing interest in approaches to information extraction, prediction, intelligent linkage, and dimension reduction applicable to large text corpora.
Pachinko Prediction: A Bayesian method for event prediction from social media data
The combination of large open data sources with machine learning approaches presents a potentially powerful way to predict events such as protest or social unrest.
