David Pinto
Impact in
- Artificial Intelligence top 1%
- Topic Modeling
- Natural Language Processing Techniques
- Advanced Text Analysis Techniques
- Text and Document Classification Technologies
- Authorship Attribution and Profiling
- Sentiment Analysis and Opinion Mining
- Information Systems top 2%
- Web Data Mining and Analysis
Papers in
-
- Topic Modeling 52
- Natural Language Processing Techniques 43
- Advanced Text Analysis Techniques 21
- Authorship Attribution and Profiling 13
- Text and Document Classification Technologies 13
- Semantic Web and Ontologies 8
- Speech and dialogue systems 6
-
- Web Data Mining and Analysis 16
- Co-authors
- Helena Gómez-Adorno (15 shared papers)Grigori Sidorov (10 shared papers)Alexander Gelbukh (4 shared papers)Xing Wei (4 shared papers)Andrew McCallum (3 shared papers)W. Bruce Croft (3 shared papers)Vivek Kumar Singh (10 shared papers)Paolo Rosso (14 shared papers)
In The Last Decade
David Pinto
97 papers receiving 1.1k citations
Peers
Comparison fields: 5 of 111
- Artificial Intelligence 856
- Information Systems 367
- Management Science and Operations Research 85
- Computer Vision and Pattern Recognition 123
- Signal Processing 63
Countries citing papers authored by David Pinto
This map shows the geographic impact of David Pinto'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 David Pinto with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Pinto more than expected).
Fields of papers citing papers by David Pinto
This network shows the impact of papers produced by David Pinto. 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 David Pinto. The network helps show where David Pinto may publish in the future.
Co-authors
The 25 scholars most cited alongside David Pinto, 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 111 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2014 | 244 | |
| 2 | 2003 | 224 | |
| 3 | 2002 | 66 | |
| 4 | 2014 | 47 | |
| 5 | 2008 | 42 | |
| 6 | 2016 | 42 | |
| 7 | 2003 | 38 | |
| 8 | 2015 | 32 | |
| 9 | 2009 | 32 | |
| 10 | 2015 | 28 | |
| 11 | 2016 | 26 | |
| 12 | 2016 | 23 | |
| 13 | 2010 | 23 | |
| 14 | 2018 | 22 | |
| 15 | 2003 | 19 | |
| 16 | 2020 | 19 | |
| 17 | 2010 | 19 | |
| 18 | 2013 | 18 | |
| 19 | 2015 | 17 | |
| 20 | 2020 | 16 |
About David Pinto
David Pinto is a scholar working on Artificial Intelligence, Information Systems, Molecular Biology, Signal Processing and Statistical and Nonlinear Physics, having authored 111 papers that have together received 1.2k indexed citations. Recurring topics across this work include Topic Modeling (52 papers), Natural Language Processing Techniques (43 papers), Advanced Text Analysis Techniques (21 papers), Web Data Mining and Analysis (16 papers), Authorship Attribution and Profiling (13 papers), Text and Document Classification Technologies (13 papers), Semantic Web and Ontologies (8 papers) and Speech and dialogue systems (6 papers). The work is most often cited by research in Artificial Intelligence (856 citations), Information Systems (367 citations), Management Science and Operations Research (85 citations), Computer Vision and Pattern Recognition (123 citations) and Signal Processing (63 citations). David Pinto has collaborated with scholars based in Mexico, India and Spain. Frequent co-authors include Helena Gómez-Adorno, Grigori Sidorov, Alexander Gelbukh, Xing Wei, Andrew McCallum, W. Bruce Croft, Vivek Kumar Singh, Paolo Rosso, Ashraf Uddin and Alfons Juan. Their work appears in journals such as Journal of Intelligent & Fuzzy Systems, Scientometrics, Applied Intelligence, Language Resources and Evaluation and The Computer Journal.
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.