Orestes Appel
Impact in
- Artificial Intelligence top 5%
- Sentiment Analysis and Opinion Mining
- Advanced Text Analysis Techniques
- Topic Modeling
- Text and Document Classification Technologies
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- Stock Market Forecasting Methods
- Multi-Criteria Decision Making
Papers in
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- Sentiment Analysis and Opinion Mining 7
- Advanced Text Analysis Techniques 4
- Text and Document Classification Technologies 2
- Topic Modeling 1
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- Rough Sets and Fuzzy Logic 3
- Co-authors
- Francisco Chiclana (7 shared papers)Jenny Carter (7 shared papers)Hamido Fujita (6 shared papers)
- Journals
- Knowledge-Based Systems (2 papers)International Journal of Intelligent Systems (1 paper)Acta Polytechnica Hungarica (1 paper)Applied Intelligence (1 paper)DMU Open Research Archive (De Montfort University) (2 papers)
- Partner nations
- United KingdomJapanCanada
In The Last Decade
Orestes Appel
7 papers receiving 287 citations
Peers
Comparison fields: 5 of 35
- Artificial Intelligence 267
- Management Science and Operations Research 46
- Information Systems 79
- Marketing 13
- Computational Theory and Mathematics 17
Countries citing papers authored by Orestes Appel
This map shows the geographic impact of Orestes Appel'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 Orestes Appel with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Orestes Appel more than expected).
Fields of papers citing papers by Orestes Appel
This network shows the impact of papers produced by Orestes Appel. 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 Orestes Appel. The network helps show where Orestes Appel may publish in the future.
Co-authors
The 3 scholars most cited alongside Orestes Appel, 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 | 2016 | 161 | |
| 2 | 2017 | 33 | |
| 3 | 2017 | 33 | |
| 4 | 2015 | 30 | |
| 5 | 2017 | 23 | |
| 6 | 2016 | 19 | |
| 7 | 2017 | 2 |
About Orestes Appel
Orestes Appel is a scholar working on Artificial Intelligence, Computational Theory and Mathematics, Management Science and Operations Research, Information Systems and Infectious Diseases, having authored 7 papers that have together received 301 indexed citations. Recurring topics across this work include Sentiment Analysis and Opinion Mining (7 papers), Advanced Text Analysis Techniques (4 papers), Rough Sets and Fuzzy Logic (3 papers), Multi-Criteria Decision Making (2 papers), Text and Document Classification Technologies (2 papers), Spam and Phishing Detection (1 paper), Stock Market Forecasting Methods (1 paper) and Topic Modeling (1 paper). The work is most often cited by research in Artificial Intelligence (267 citations), Management Science and Operations Research (46 citations), Information Systems (79 citations), Marketing (13 citations) and Computational Theory and Mathematics (17 citations). Orestes Appel has collaborated with scholars based in United Kingdom, Japan and Canada. Frequent co-authors include Francisco Chiclana, Jenny Carter and Hamido Fujita. Their work appears in journals such as Knowledge-Based Systems, International Journal of Intelligent Systems, Acta Polytechnica Hungarica, Applied Intelligence and DMU Open Research Archive (De Montfort University).
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.