Vu Tran
Impact in
- Artificial Intelligence top 5%
- Topic Modeling
- Natural Language Processing Techniques
- Advanced Text Analysis Techniques
- Sentiment Analysis and Opinion Mining
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- Artificial Intelligence in Law
Papers in
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- Topic Modeling 26
- Natural Language Processing Techniques 22
- Advanced Text Analysis Techniques 8
- Sentiment Analysis and Opinion Mining 4
- Speech and dialogue systems 2
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- Artificial Intelligence in Law 8
- Co-authors
- Le-Minh Nguyen (27 shared papers)Minh-Tien Nguyen (9 shared papers)Ha-Thanh Nguyen (9 shared papers)Ken Satoh (6 shared papers)Tu Vu (1 shared paper)Quan Hung Tran (1 shared paper)Son Bao Pham (1 shared paper)Satoshi Tojo (1 shared paper)
In The Last Decade
Vu Tran
30 papers receiving 305 citations
Peers
Comparison fields: 5 of 52
- Artificial Intelligence 250
- Political Science and International Relations 107
- Information Systems 66
- Health Informatics 3
- Law 23
Countries citing papers authored by Vu Tran
This map shows the geographic impact of Vu Tran'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 Vu Tran with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Vu Tran more than expected).
Fields of papers citing papers by Vu Tran
This network shows the impact of papers produced by Vu Tran. 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 Vu Tran. The network helps show where Vu Tran may publish in the future.
Co-authors
The 25 scholars most cited alongside Vu Tran, 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 35 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2015 | 58 | |
| 2 | 2020 | 36 | |
| 3 | 2020 | 33 | |
| 4 | 2022 | 22 | |
| 5 | 2022 | 19 | |
| 6 | 2025 | 15 | |
| 7 | 2016 | 12 | |
| 8 | 2017 | 12 | |
| 9 | 2022 | 11 | |
| 10 | 2021 | 10 | |
| 11 | 2022 | 9 | |
| 12 | VSoLSCSum: Building a Vietnamese Sentence-Comment Dataset for Social Context Summarization | 2016 | 8 |
| 13 | 2022 | 8 | |
| 14 | 2019 | 7 | |
| 15 | 2019 | 7 | |
| 16 | 2016 | 7 | |
| 17 | 2017 | 5 | |
| 18 | 2023 | 5 | |
| 19 | 2023 | 5 | |
| 20 | 2023 | 5 |
About Vu Tran
Vu Tran is a scholar working on Artificial Intelligence, Political Science and International Relations, Sociology and Political Science, Computer Vision and Pattern Recognition and Information Systems, having authored 35 papers that have together received 319 indexed citations. Recurring topics across this work include Topic Modeling (26 papers), Natural Language Processing Techniques (22 papers), Artificial Intelligence in Law (8 papers), Advanced Text Analysis Techniques (8 papers), Sentiment Analysis and Opinion Mining (4 papers), Misinformation and Its Impacts (4 papers), Multimodal Machine Learning Applications (3 papers) and Speech and dialogue systems (2 papers). The work is most often cited by research in Artificial Intelligence (250 citations), Political Science and International Relations (107 citations), Information Systems (66 citations), Health Informatics (3 citations) and Law (23 citations). Vu Tran has collaborated with scholars based in Japan, Vietnam and Belgium. Frequent co-authors include Le-Minh Nguyen, Minh-Tien Nguyen, Ha-Thanh Nguyen, Ken Satoh, Tu Vu, Quan Hung Tran, Son Bao Pham, Satoshi Tojo, Từ Minh Phương and Ngo Xuan Bach. Their work appears in journals such as Artificial Intelligence and Law, Frontiers in Public Health, Knowledge-Based Systems, Atmosphere and ACM Transactions on Knowledge Discovery from Data.
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.