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 language | English (US) |
|---|---|
| Title of host publication | Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015 |
| Publisher | Association for Computing Machinery, Inc |
| Pages | 1569-1570 |
| Number of pages | 2 |
| ISBN (Electronic) | 9781450338547 |
| DOIs | |
| State | Published - Aug 25 2015 |
| Event | IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015 - Paris, France Duration: Aug 25 2015 → Aug 28 2015 |
Other
| Other | IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015 |
|---|---|
| Country | France |
| City | Paris |
| Period | 8/25/15 → 8/28/15 |
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ASJC Scopus subject areas
- Computer Science Applications
- Computer Networks and Communications
Cite this
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AttitudeBuzz : Using social media data to localize complex attitudes. / Cohn, Jason; Kuntz, Alex; Birnbaum, Lawrence A.
Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015. Association for Computing Machinery, Inc, 2015. p. 1569-1570.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - AttitudeBuzz
T2 - Using social media data to localize complex attitudes
AU - Cohn, Jason
AU - Kuntz, Alex
AU - Birnbaum, Lawrence A
PY - 2015/8/25
Y1 - 2015/8/25
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84962523352&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84962523352&partnerID=8YFLogxK
U2 - 10.1145/2808797.2809336
DO - 10.1145/2808797.2809336
M3 - Conference contribution
SP - 1569
EP - 1570
BT - Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015
PB - Association for Computing Machinery, Inc
ER -