Felipe Viegas

845 total citations
27 papers, 543 citations indexed

About

Felipe Viegas is a scholar working on Artificial Intelligence, Information Systems and General Social Sciences. According to data from OpenAlex, Felipe Viegas has authored 27 papers receiving a total of 543 indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Artificial Intelligence, 7 papers in Information Systems and 5 papers in General Social Sciences. Recurrent topics in Felipe Viegas's work include Text and Document Classification Technologies (15 papers), Topic Modeling (13 papers) and Advanced Text Analysis Techniques (10 papers). Felipe Viegas is often cited by papers focused on Text and Document Classification Technologies (15 papers), Topic Modeling (13 papers) and Advanced Text Analysis Techniques (10 papers). Felipe Viegas collaborates with scholars based in Brazil, Portugal and United States. Felipe Viegas's co-authors include Leonardo Rocha, Marcos André Gonçalves, Thiago Salles, Thierson Couto Rosa, Sérgio Canuto, Fernando Mourão, Christian Gomes, Marlus Chorilli, Delia Rita Tapia‐Blácido and Juliana Palma Abriata and has published in prestigious journals such as SHILAP Revista de lepidopterología, ACM Computing Surveys and Information Sciences.

In The Last Decade

Felipe Viegas

24 papers receiving 529 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Felipe Viegas Brazil	11	321	111	60	46	35	27	543
Shivam Agarwal India	10	152 0.5×	115 1.0×	17 0.3×	104 2.3×	6 0.2×	42	431
Rajesh Prasad India	10	205 0.6×	78 0.7×	73 1.2×	5 0.1×	4 0.1×	97	502
Meena Nagarajan India	10	148 0.5×	56 0.5×	86 1.4×	6 0.1×	5 0.1×	24	501
Hai Wang United Kingdom	13	210 0.7×	189 1.7×	66 1.1×	36 0.8×		63	638
Yilei Wang China	12	228 0.7×	273 2.5×	16 0.3×	9 0.2×		39	569
Tahir Ahmad Pakistan	11	101 0.3×	112 1.0×	31 0.5×	5 0.1×		33	363
Howard Ho United States	12	263 0.8×	140 1.3×	58 1.0×	78 1.7×		21	510
Arushi Jain India	9	46 0.1×	54 0.5×	73 1.2×	68 1.5×	1 0.0×	24	369
Jiajun Cheng China	9	254 0.8×	56 0.5×	20 0.3×	1 0.0×	7 0.2×	30	452

Countries citing papers authored by Felipe Viegas

Since Specialization

Citations

This map shows the geographic impact of Felipe Viegas'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 Felipe Viegas with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Felipe Viegas more than expected).

Fields of papers citing papers by Felipe Viegas

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Felipe Viegas. 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 Felipe Viegas. The network helps show where Felipe Viegas may publish in the future.

Co-authorship network of co-authors of Felipe Viegas

This figure shows the co-authorship network connecting the top 25 collaborators of Felipe Viegas. A scholar is included among the top collaborators of Felipe Viegas based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Felipe Viegas. Felipe Viegas is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Viegas, Felipe, et al.. (2025). “Are the current topic modeling evaluation metrics enough?” Mitigating the limitations of topic modeling evaluation metrics using a multi-perspective game theoretic approach. Knowledge-Based Systems. 320. 113634–113634.

Viegas, Felipe, et al.. (2024). Exploiting Contextual Embeddings in Hierarchical Topic Modeling and Investigating the Limits of the Current Evaluation Metrics. Computational Linguistics. 51(3). 843–883. 1 indexed citations

Viegas, Felipe, et al.. (2024). Pipelining Semantic Expansion and Noise Filtering for Sentiment Analysis of Short Documents – CluSent Method. SHILAP Revista de lepidopterología. 15(1). 561–575.

Belém, Fabiano, et al.. (2023). On the class separability of contextual embeddings representations – or “The classifier does not matter when the (text) representation is so good!”. Information Processing & Management. 60(4). 103336–103336. 18 indexed citations

Viegas, Felipe, et al.. (2023). CluSent – Combining Semantic Expansion and De-Noising for Dataset-Oriented Sentiment Analysis of Short Texts. 110–118. 2 indexed citations

Viegas, Felipe, et al.. (2023). A Comparative Survey of Instance Selection Methods applied to Non-Neural and Transformer-Based Text Classification. ACM Computing Surveys. 55(13s). 1–52. 24 indexed citations

Viegas, Felipe, et al.. (2023). Evaluating the Limits of the Current Evaluation Metrics for Topic Modeling. 119–127. 1 indexed citations

Viegas, Felipe, et al.. (2022). Semantic Academic Profiler (SAP): a framework for researcher assessment based on semantic topic modeling. Scientometrics. 127(8). 5005–5026. 4 indexed citations

Viegas, Felipe, et al.. (2022). Evaluating Topic Modeling Pre-processing Pipelines for Portuguese Texts. 191–201. 8 indexed citations

10.

Etges, Ana Paula Beck da Silva, Ana Cláudia de Souza, Felipe Viegas, et al.. (2021). Stroke Outcome Measurements From Electronic Medical Records: Cross-sectional Study on the Effectiveness of Neural and Nonneural Classifiers. JMIR Medical Informatics. 9(11). e29120–e29120. 8 indexed citations

11.

Luiz, Marcela Tavares, Juliana Santos Rosa Viegas, Juliana Palma Abriata, et al.. (2021). Design of experiments (DoE) to develop and to optimize nanoparticles as drug delivery systems. European Journal of Pharmaceutics and Biopharmaceutics. 165. 127–148. 126 indexed citations

12.

Mangaravite, Vítor, Christian Gomes, Sérgio Canuto, et al.. (2021). On the cost-effectiveness of neural and non-neural approaches and representations for text classification: A comprehensive comparative study. Information Processing & Management. 58(3). 102481–102481. 56 indexed citations

13.

Viegas, Felipe, et al.. (2020). CluHTM - Semantic Hierarchical Topic Modeling based on CluWords. 8138–8150. 21 indexed citations

14.

Viegas, Felipe, Mário S. Alvim, Sérgio Canuto, et al.. (2020). Exploiting semantic relationships for unsupervised expansion of sentiment lexicons. Information Systems. 94. 101606–101606. 21 indexed citations

15.

Mourão, Fernando, Leonardo Rocha, Felipe Viegas, et al.. (2018). NetClass: A network-based relational model for document classification. Information Sciences. 469. 60–78. 1 indexed citations

16.

Viegas, Felipe, et al.. (2017). A Genetic Programming approach for feature selection in highly dimensional skewed data. Neurocomputing. 273. 554–569. 66 indexed citations

17.

Salles, Thiago, Leonardo Rocha, Fernando Mourão, et al.. (2017). A Two-Stage Machine learning approach for temporally-robust text classification. Information Systems. 69. 40–58. 7 indexed citations

18.

Viegas, Felipe, Marcos André Gonçalves, Wellington S. Martins, & Leonardo Rocha. (2015). Parallel Lazy Semi-Naive Bayes Strategies for Effective and Efficient Document Classification. 1071–1080. 5 indexed citations

19.

Rocha, Leonardo, et al.. (2015). G-KNN. 1335–1338. 5 indexed citations

20.

Viegas, Felipe, et al.. (2012). Aggressive and effective feature selection using genetic programming. Zenodo (CERN European Organization for Nuclear Research). 1–8. 16 indexed citations

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.

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