AttitudeBuzz: Using social media data to localize complex attitudes

Jason Cohn, Alex Kuntz, Lawrence A Birnbaum

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

AttitudeBuzz is a system that analyzes and presents complex social attitudes based on geolocated social media data. The system uses a machine learning model to apply highly domain-specific sentiment analysis to such data, specifically Twitter, by learning modulators around a configurable lexicon central to the domain of inquiry. Training data are acquired from geographical areas where a specific attitude or opinion is known to dominate. We apply AttitudeBuzz to the domain of homophobic attitudes expressed on Twitter. The resulting user interface is presented and the machine learning model described and analyzed.

Original languageEnglish (US)
Title of host publicationProceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015
EditorsJian Pei, Jie Tang, Fabrizio Silvestri
PublisherAssociation for Computing Machinery, Inc
Pages1569-1570
Number of pages2
ISBN (Electronic)9781450338547
DOIs
StatePublished - Aug 25 2015
EventIEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015 - Paris, France
Duration: Aug 25 2015Aug 28 2015

Publication series

NameProceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015

Other

OtherIEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015
CountryFrance
CityParis
Period8/25/158/28/15

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Networks and Communications

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