Search Results for author: Eric M. Clark

Found 4 papers, 0 papers with code

A Sentiment Analysis of Breast Cancer Treatment Experiences and Healthcare Perceptions Across Twitter

no code implementations25 May 2018 Eric M. Clark, Ted James, Chris A. Jones, Amulya Alapati, Promise Ukandu, Christopher M. Danforth, Peter Sheridan Dodds

Conclusions: Social media can provide a positive outlet for patients to discuss their needs and concerns regarding their healthcare coverage and treatment needs.

Decision Making Sentiment Analysis

Sifting Robotic from Organic Text: A Natural Language Approach for Detecting Automation on Twitter

no code implementations17 May 2015 Eric M. Clark, Jake Ryland Williams, Chris A. Jones, Richard A. Galbraith, Christopher M. Danforth, Peter Sheridan Dodds

Twitter, a popular social media outlet, has evolved into a vast source of linguistic data, rich with opinion, sentiment, and discussion.

Identifying missing dictionary entries with frequency-conserving context models

no code implementations7 Mar 2015 Jake Ryland Williams, Eric M. Clark, James P. Bagrow, Christopher M. Danforth, Peter Sheridan Dodds

With our predictions we then engage the editorial community of the Wiktionary and propose short lists of potential missing entries for definition, developing a breakthrough, lexical extraction technique, and expanding our knowledge of the defined English lexicon of phrases.

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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