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
- Semantic Web and Ontologies
- Sentiment Analysis and Opinion Mining
- Information Systems top 2%
- Web Data Mining and Analysis
Papers in
-
- Topic Modeling 68
- Natural Language Processing Techniques 65
- Advanced Text Analysis Techniques 27
- Text and Document Classification Technologies 20
- Authorship Attribution and Profiling 16
- Semantic Web and Ontologies 16
- Algorithms and Data Compression 12
-
- Web Data Mining and Analysis 25
- Co-authors
- Helena Gómez-Adorno (20 shared papers)Grigori Sidorov (12 shared papers)Paolo Rosso (27 shared papers)Alexander Gelbukh (4 shared papers)Xing Wei (4 shared papers)W. Bruce Croft (3 shared papers)Andrew McCallum (3 shared papers)Vivek Kumar Singh (11 shared papers)
In The Last Decade
David Pinto
140 papers receiving 1.6k citations
Peers
Comparison fields: 5 of 119
- Artificial Intelligence 1.3k
- Information Systems 549
- Signal Processing 99
- Management Science and Operations Research 108
- Computer Vision and Pattern Recognition 174
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 161 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2014 | 280 | |
| 2 | 2003 | 271 | |
| 3 | 2002 | 73 | |
| 4 | 2008 | 55 | |
| 5 | 2016 | 55 | |
| 6 | 2014 | 50 | |
| 7 | 2009 | 40 | |
| 8 | 2007 | 40 | |
| 9 | 2003 | 39 | |
| 10 | 2015 | 33 | |
| 11 | 2016 | 32 | |
| 12 | 2006 | 32 | |
| 13 | 2010 | 31 | |
| 14 | 2008 | 31 | |
| 15 | 2015 | 28 | |
| 16 | 2018 | 27 | |
| 17 | 2016 | 26 | |
| 18 | 2010 | 25 | |
| 19 | 2015 | 21 | |
| 20 | 2003 | 19 |
About David Pinto
David Pinto is a scholar working on Artificial Intelligence, Information Systems, Signal Processing, Molecular Biology and Computer Vision and Pattern Recognition, having authored 161 papers that have together received 1.7k indexed citations. Recurring topics across this work include Topic Modeling (68 papers), Natural Language Processing Techniques (65 papers), Advanced Text Analysis Techniques (27 papers), Web Data Mining and Analysis (25 papers), Text and Document Classification Technologies (20 papers), Authorship Attribution and Profiling (16 papers), Semantic Web and Ontologies (16 papers) and Algorithms and Data Compression (12 papers). The work is most often cited by research in Artificial Intelligence (1.3k citations), Information Systems (549 citations), Signal Processing (99 citations), Management Science and Operations Research (108 citations) and Computer Vision and Pattern Recognition (174 citations). David Pinto has collaborated with scholars based in Mexico, Spain and India. Frequent co-authors include Helena Gómez-Adorno, Grigori Sidorov, Paolo Rosso, Alexander Gelbukh, Xing Wei, W. Bruce Croft, Andrew McCallum, Vivek Kumar Singh, Alfons Juan and Alberto Barrón‐Cedeño. Their work appears in journals such as Journal of Intelligent & Fuzzy Systems, Lecture notes in computer science, Scientometrics, Pattern Recognition Letters 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.