Aline Paes
Impact in
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- Digital Mental Health Interventions
- Artificial Intelligence top 10%
- Sentiment Analysis and Opinion Mining
- Topic Modeling
- Bayesian Modeling and Causal Inference
Papers in
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- Topic Modeling 17
- Sentiment Analysis and Opinion Mining 16
- Natural Language Processing Techniques 12
- Semantic Web and Ontologies 7
- Artificial Intelligence in Games 6
- Bayesian Modeling and Causal Inference 6
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- Distributed and Parallel Computing Systems 6
- Co-authors
- Daniel de Oliveira (20 shared papers)Gerson Zaverucha (10 shared papers)Flávia Bernardini (3 shared papers)Esteban Clua (9 shared papers)Cláudio Tinoco Mesquita (1 shared paper)Flávio Luiz Seixas (1 shared paper)Alexandre Plastino (5 shared papers)Antônio A. de A. Rocha (3 shared papers)
In The Last Decade
Aline Paes
58 papers receiving 294 citations
Peers
Comparison fields: 5 of 85
- Applied Psychology 22
- Artificial Intelligence 146
- Ocean Engineering 49
- Information Systems and Management 20
- Information Systems 49
Countries citing papers authored by Aline Paes
This map shows the geographic impact of Aline Paes'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 Aline Paes with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Aline Paes more than expected).
Fields of papers citing papers by Aline Paes
This network shows the impact of papers produced by Aline Paes. 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 Aline Paes. The network helps show where Aline Paes may publish in the future.
Co-authors
The 25 scholars most cited alongside Aline Paes, 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 72 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2023 | 57 | |
| 2 | 2020 | 27 | |
| 3 | 2020 | 25 | |
| 4 | 2021 | 11 | |
| 5 | 2024 | 10 | |
| 6 | 2022 | 10 | |
| 7 | 2019 | 10 | |
| 8 | 2018 | 9 | |
| 9 | 2020 | 8 | |
| 10 | 2009 | 7 | |
| 11 | 2021 | 7 | |
| 12 | 2019 | 7 | |
| 13 | 2018 | 7 | |
| 14 | 2019 | 7 | |
| 15 | 2022 | 5 | |
| 16 | 2019 | 5 | |
| 17 | 2020 | 5 | |
| 18 | 2019 | 5 | |
| 19 | 2020 | 5 | |
| 20 | 2023 | 4 |
About Aline Paes
Aline Paes is a scholar working on Artificial Intelligence, Computer Networks and Communications, Information Systems, Computer Vision and Pattern Recognition and Information Systems and Management, having authored 72 papers that have together received 303 indexed citations. Recurring topics across this work include Topic Modeling (17 papers), Sentiment Analysis and Opinion Mining (16 papers), Natural Language Processing Techniques (12 papers), Scientific Computing and Data Management (10 papers), Semantic Web and Ontologies (7 papers), Artificial Intelligence in Games (6 papers), Bayesian Modeling and Causal Inference (6 papers) and Distributed and Parallel Computing Systems (6 papers). The work is most often cited by research in Applied Psychology (22 citations), Artificial Intelligence (146 citations), Ocean Engineering (49 citations), Information Systems and Management (20 citations) and Information Systems (49 citations). Aline Paes has collaborated with scholars based in Brazil, France and Portugal. Frequent co-authors include Daniel de Oliveira, Gerson Zaverucha, Flávia Bernardini, Esteban Clua, Cláudio Tinoco Mesquita, Flávio Luiz Seixas, Alexandre Plastino, Antônio A. de A. Rocha, Leonardo Murta and Vı́tor Santos Costa. Their work appears in journals such as Machine Learning, Information Processing & Management, Neural Computing and Applications, Applied Intelligence and PeerJ Computer Science.
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.