Search Results for author: Andrew J. Reagan

Found 10 papers, 5 papers with code

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.

Storywrangler: A massive exploratorium for sociolinguistic, cultural, socioeconomic, and political timelines using Twitter

6 code implementations25 Jul 2020 Thayer Alshaabi, Jane L. Adams, Michael V. Arnold, Joshua R. Minot, David R. Dewhurst, Andrew J. Reagan, Christopher M. Danforth, Peter Sheridan Dodds

In real-time, social media data strongly imprints world events, popular culture, and day-to-day conversations by millions of ordinary people at a scale that is scarcely conventionalized and recorded.

Time Series

English verb regularization in books and tweets

no code implementations26 Mar 2018 Tyler J. Gray, Andrew J. Reagan, Peter Sheridan Dodds, Christopher M. Danforth

We find that the extent of verb regularization is greater on Twitter, taken as a whole, than in English Fiction books.


Towards a science of human stories: using sentiment analysis and emotional arcs to understand the building blocks of complex social systems

no code implementations17 Dec 2017 Andrew J. Reagan

Given the growing assortment of sentiment measuring instruments, it is imperative to understand which aspects of sentiment dictionaries contribute to both their classification accuracy and their ability to provide richer understanding of texts.

General Classification Sentiment Analysis

Forecasting the onset and course of mental illness with Twitter data

1 code implementation27 Aug 2016 Andrew G. Reece, Andrew J. Reagan, Katharina L. M. Lix, Peter Sheridan Dodds, Christopher M. Danforth, Ellen J. Langer

Twitter data and details of depression history were collected from 204 individuals (105 depressed, 99 healthy).

Physics and Society Social and Information Networks

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.

Divergent discourse between protests and counter-protests: #BlackLivesMatter and #AllLivesMatter

no code implementations22 Jun 2016 Ryan J. Gallagher, Andrew J. Reagan, Christopher M. Danforth, Peter Sheridan Dodds

Since the shooting of Black teenager Michael Brown by White police officer Darren Wilson in Ferguson, Missouri, the protest hashtag #BlackLivesMatter has amplified critiques of extrajudicial killings of Black Americans.

Benchmarking sentiment analysis methods for large-scale texts: A case for using continuum-scored words and word shift graphs

2 code implementations2 Dec 2015 Andrew J. Reagan, Brian Tivnan, Jake Ryland Williams, Christopher M. Danforth, Peter Sheridan Dodds

The emergence and global adoption of social media has rendered possible the real-time estimation of population-scale sentiment, bearing profound implications for our understanding of human behavior.

Sentiment Analysis

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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