Search Results for author: Lewis Mitchell

Found 11 papers, 4 papers with code

Life still goes on: Analysing Australian WW1 Diaries through Distant Reading

no code implementations COLING (LaTeCHCLfL, CLFL, LaTeCH) 2020 Ashley Dennis-Henderson, Matthew Roughan, Lewis Mitchell, Jonathan Tuke

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.

Sentiment Analysis

Revealing Patient-Reported Experiences in Healthcare from Social Media using the DAPMAV Framework

no code implementations9 Oct 2022 Curtis Murray, Lewis Mitchell, Jonathan Tuke, Mark Mackay

Understanding patient experience in healthcare is increasingly important and desired by medical professionals in a patient-centred care approach.

Generalized Word Shift Graphs: A Method for Visualizing and Explaining Pairwise Comparisons Between Texts

3 code implementations5 Aug 2020 Ryan J. Gallagher, Morgan R. Frank, Lewis Mitchell, Aaron J. Schwartz, Andrew J. Reagan, Christopher M. Danforth, Peter Sheridan Dodds

A common task in computational text analyses is to quantify how two corpora differ according to a measurement like word frequency, sentiment, or information content.

Symptom extraction from the narratives of personal experiences with COVID-19 on Reddit

1 code implementation21 May 2020 Curtis Murray, Lewis Mitchell, Jonathan Tuke, Mark Mackay

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.

Sentiment Analysis

A framework for streamlined statistical prediction using topic models

no code implementations WS 2019 Vanessa Glenny, Jonathan Tuke, Nigel Bean, Lewis Mitchell

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.

Dimensionality Reduction Topic Models

Event detection in Twitter: A keyword volume approach

1 code implementation3 Jan 2019 Ahmad Hany Hossny, Lewis Mitchell

In this paper, we propose an efficient method to select the keywords frequently used in Twitter that are mostly associated with events of interest such as protests.

Event Detection Transliteration

Pachinko Prediction: A Bayesian method for event prediction from social media data

no code implementations22 Sep 2018 Jonathan Tuke, Andrew Nguyen, Mehwish Nasim, Drew Mellor, Asanga Wickramasinghe, Nigel Bean, Lewis Mitchell

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.

BIG-bench Machine Learning

Enhancing keyword correlation for event detection in social networks using SVD and k-means: Twitter case study

no code implementations25 Jul 2018 Ahmad Hany Hossny, Terry Moschou, Grant Osborne, Lewis Mitchell, Nick Lothian

The lookup table is used to map each feature in the original data to the centroid of its cluster, then we calculate the sum of the term frequency vectors of all features in each cluster to the term-frequency-vector of the cluster centroid.

Event Detection Time Series

The emotional arcs of stories are dominated by six basic shapes

2 code implementations24 Jun 2016 Andrew J. Reagan, Lewis Mitchell, Dilan Kiley, Christopher M. Danforth, Peter Sheridan Dodds

Advances in computing power, natural language processing, and digitization of text now make it possible to study a culture's evolution through its texts using a "big data" lens.

Human language reveals a universal positivity bias

no code implementations15 Jun 2014 Peter Sheridan Dodds, Eric M. Clark, Suma Desu, Morgan R. Frank, Andrew J. Reagan, Jake Ryland Williams, Lewis Mitchell, Kameron Decker Harris, Isabel M. Kloumann, James P. Bagrow, Karine Megerdoomian, Matthew T. McMahon, Brian F. Tivnan, Christopher M. Danforth

Using human evaluation of 100, 000 words spread across 24 corpora in 10 languages diverse in origin and culture, we present evidence of a deep imprint of human sociality in language, observing that (1) the words of natural human language possess a universal positivity bias; (2) the estimated emotional content of words is consistent between languages under translation; and (3) this positivity bias is strongly independent of frequency of word usage.


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