Peter Eklund
Impact in
- Artificial Intelligence top 1%
- Semantic Web and Ontologies
- Sentiment Analysis and Opinion Mining
- Signal Processing top 2%
- Data Management and Algorithms
Papers in
-
- Semantic Web and Ontologies 48
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- Rough Sets and Fuzzy Logic 42
- Co-authors
- Richard Cole (14 shared papers)Usman Naseem (4 shared papers)Imran Razzak (5 shared papers)Philippe A. Martin (7 shared papers)Matloob Khushi (1 shared paper)Jinman Kim (1 shared paper)Gerd Stumme (4 shared papers)Frithjof Dau (5 shared papers)
- Journals
- Lecture notes in computer science (37 papers)Materials Science and Engineering C (2 papers)Applied Sciences (2 papers)Physical Review B (2 papers)Journal of Molecular Modeling (1 paper)
- Partner nations
- AustraliaUnited StatesGermany
In The Last Decade
Peter Eklund
139 papers receiving 2.5k citations
Peter Eklund's Hit Papers
Peers
Comparison fields: 5 of 148
- Artificial Intelligence 1.3k
- Signal Processing 392
- Computational Theory and Mathematics 527
- Information Systems 721
- Computer Vision and Pattern Recognition 366
Countries citing papers authored by Peter Eklund
This map shows the geographic impact of Peter Eklund'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 Peter Eklund with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Peter Eklund more than expected).
Fields of papers citing papers by Peter Eklund
This network shows the impact of papers produced by Peter Eklund. 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 Peter Eklund. The network helps show where Peter Eklund may publish in the future.
Co-authors
The 25 scholars most cited alongside Peter Eklund, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 150 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | COVIDSenti: A Large-Scale Benchmark Twitter Data Set for COVID-19 Sentiment Analysis Hit paper breakdown → | 2021 | 248 |
| 2 | 2009 | 162 | |
| 3 | 2005 | 121 | |
| 4 | 2008 | 117 | |
| 5 | 1998 | 115 | |
| 6 | 2021 | 89 | |
| 7 | 2020 | 83 | |
| 8 | 1999 | 74 | |
| 9 | Conceptual structures: current research and practice | 1992 | 72 |
| 10 | 2021 | 68 | |
| 11 | 2003 | 66 | |
| 12 | 2003 | 64 | |
| 13 | 2000 | 63 | |
| 14 | 2022 | 49 | |
| 15 | 2016 | 41 | |
| 16 | 1999 | 40 | |
| 17 | 1998 | 39 | |
| 18 | 2001 | 38 | |
| 19 | 2002 | 38 | |
| 20 | 2008 | 37 |
About Peter Eklund
Peter Eklund is a scholar working on Artificial Intelligence, Computational Theory and Mathematics, Signal Processing, Computer Vision and Pattern Recognition and Information Systems, having authored 150 papers that have together received 2.7k indexed citations. Recurring topics across this work include Semantic Web and Ontologies (48 papers), Rough Sets and Fuzzy Logic (42 papers), Data Management and Algorithms (22 papers), Image Retrieval and Classification Techniques (16 papers), Advanced Database Systems and Queries (14 papers), Data Mining Algorithms and Applications (12 papers), Biomedical Text Mining and Ontologies (10 papers) and Data Visualization and Analytics (8 papers). The work is most often cited by research in Artificial Intelligence (1.3k citations), Signal Processing (392 citations), Computational Theory and Mathematics (527 citations), Information Systems (721 citations) and Computer Vision and Pattern Recognition (366 citations). Peter Eklund has collaborated with scholars based in Australia, United States and Germany. Frequent co-authors include Richard Cole, Usman Naseem, Imran Razzak, Philippe A. Martin, Matloob Khushi, Jinman Kim, Gerd Stumme, Frithjof Dau, Peter Becker and G. Dresselhaus. Their work appears in journals such as Lecture notes in computer science, Materials Science and Engineering C, Applied Sciences, Physical Review B and Journal of Molecular Modeling.
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.