Fu Lee Wang
Impact in
- Health Informatics top 2%
-
- Online Learning and Analytics
Papers in
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- Online Learning and Analytics 27
-
- Topic Modeling 60
- Advanced Text Analysis Techniques 38
- Sentiment Analysis and Opinion Mining 34
- Natural Language Processing Techniques 28
- Text and Document Classification Technologies 23
- Co-authors
- Haoran XieDi ZouYanghui RaoXieling ChenMingqiang WeiQing LiChristopher C. YangTak-Lam Wong
In The Last Decade
Fu Lee Wang
220 papers receiving 3.2k citations
Hit Papers
Peers
Comparison fields: 5 of 177
- Health Informatics 87
- Computer Science Applications 311
- Artificial Intelligence 1.4k
- Information Systems 762
- Computer Vision and Pattern Recognition 654
Countries citing papers authored by Fu Lee Wang
This map shows the geographic impact of Fu Lee Wang'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 Fu Lee Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fu Lee Wang more than expected).
Fields of papers citing papers by Fu Lee Wang
This network shows the impact of papers produced by Fu Lee Wang. 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 Fu Lee Wang. The network helps show where Fu Lee Wang may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Fu Lee Wang, 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 | 2025 | 2 | |
| 2 | 2025 | 2 | |
| 3 | 2025 | 0 | |
| 4 | 2025 | 1 | |
| 5 | 2025 | 4 | |
| 6 | 2024 | 19 | |
| 7 | 2024 | 5 | |
| 8 | 2024 | 7 | |
| 9 | 2024 | 1 | |
| 10 | 2024 | 0 | |
| 11 | 2023 | 5 | |
| 12 | 2023 | 2 | |
| 13 | 2023 | 17 | |
| 14 | 2022 | 12 | |
| 15 | 2022 | 25 | |
| 16 | 2022 | 31 | |
| 17 | 2022 | 12 | |
| 18 | 2021 | 6 | |
| 19 | 2020 | 23 | |
| 20 | 2018 | 3 |
About Fu Lee Wang
Fu Lee Wang is a scholar working on Computer Science Applications, Artificial Intelligence, Computer Graphics and Computer-Aided Design, Health Informatics and General Social Sciences, having authored 243 papers that have together received 3.4k indexed citations. Recurring topics across this work include Topic Modeling (60 papers), Advanced Text Analysis Techniques (38 papers), Sentiment Analysis and Opinion Mining (34 papers), Natural Language Processing Techniques (28 papers), Online Learning and Analytics (27 papers), Text and Document Classification Technologies (23 papers), Recommender Systems and Techniques (13 papers) and 3D Shape Modeling and Analysis (12 papers). The work is most often cited by research in Health Informatics (87 citations), Computer Science Applications (311 citations), Artificial Intelligence (1.4k citations), Information Systems (762 citations) and Computer Vision and Pattern Recognition (654 citations). Fu Lee Wang has collaborated with scholars based in Hong Kong, China and Australia. Frequent co-authors include Haoran Xie, Di Zou, Yanghui Rao, Xieling Chen, Mingqiang Wei, Qing Li, Christopher C. Yang, Tak-Lam Wong, Gary Cheng and Jun Wang. Their work appears in journals such as Neurocomputing, International Journal of Machine Learning and Cybernetics, Information Processing & Management, Neural Computing and Applications and International Journal of Innovation and Learning.
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.