Sean Gerrish
Impact in
- General Social Sciences top 0.02%
- Computational and Text Analysis Methods
- Artificial Intelligence top 2%
- Topic Modeling
- Advanced Text Analysis Techniques
- Natural Language Processing Techniques
- Sentiment Analysis and Opinion Mining
- Text and Document Classification Technologies
Papers in
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- Computational and Text Analysis Methods 3
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- Advanced Combinatorial Mathematics 2
- Co-authors
- David M. Blei (6 shared papers)Chong Wang (1 shared paper)Jonathan Chang (1 shared paper)Jordan Lee Boyd-Graber (1 shared paper)Michael J. Freedman (1 shared paper)Prem Gopalan (1 shared paper)David Mimno (1 shared paper)Lada A. Adamic (1 shared paper)
- Journals
- The Electronic Journal of Combinatorics (2 papers)First Monday (1 paper)International Conference on Machine Learning (2 papers)Neural Information Processing Systems (3 papers)
- Partner nations
- United States
In The Last Decade
Sean Gerrish
9 papers receiving 1.6k citations
Sean Gerrish's Hit Papers
Peers
Comparison fields: 5 of 130
- General Social Sciences 399
- Artificial Intelligence 991
- Communication 137
- Statistical and Nonlinear Physics 226
- Information Systems 259
Countries citing papers authored by Sean Gerrish
This map shows the geographic impact of Sean Gerrish's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Sean Gerrish with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sean Gerrish more than expected).
Fields of papers citing papers by Sean Gerrish
This network shows the impact of papers produced by Sean Gerrish. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Sean Gerrish. The network helps show where Sean Gerrish may publish in the future.
Co-authors
The 12 scholars most cited alongside Sean Gerrish, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Reading Tea Leaves: How Humans Interpret Topic Models Hit paper breakdown → | 2009 | 1361 |
| 2 | Predicting Legislative Roll Calls from Text | 2011 | 100 |
| 3 | A Language-based Approach to Measuring Scholarly Impact | 2010 | 92 |
| 4 | How They Vote: Issue-Adjusted Models of Legislative Behavior | 2012 | 60 |
| 5 | Scalable Inference of Overlapping Communities | 2012 | 55 |
| 6 | 2010 | 16 | |
| 7 | 2004 | 13 | |
| 8 | 2005 | 5 | |
| 9 | Applications of latent variable models in modeling influence and decision making | 2013 | 2 |
About Sean Gerrish
Sean Gerrish is a scholar working on General Social Sciences, Discrete Mathematics and Combinatorics, Algebra and Number Theory, Artificial Intelligence and Political Science and International Relations, having authored 9 papers that have together received 1.7k indexed citations. Recurring topics across this work include Computational and Text Analysis Methods (3 papers), Bayesian Methods and Mixture Models (2 papers), Advanced Text Analysis Techniques (2 papers), Electoral Systems and Political Participation (2 papers), Advanced Combinatorial Mathematics (2 papers), Topic Modeling (2 papers), Advanced Mathematical Identities (2 papers) and Judicial and Constitutional Studies (1 paper). The work is most often cited by research in General Social Sciences (399 citations), Artificial Intelligence (991 citations), Communication (137 citations), Statistical and Nonlinear Physics (226 citations) and Information Systems (259 citations). Sean Gerrish has collaborated with scholars based in United States. Frequent co-authors include David M. Blei, Chong Wang, Jonathan Chang, Jordan Lee Boyd-Graber, Michael J. Freedman, Prem Gopalan, David Mimno, Lada A. Adamic, Jiang Yang and Gavin Clarkson. Their work appears in journals such as The Electronic Journal of Combinatorics, First Monday, International Conference on Machine Learning and Neural Information Processing Systems.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.