Geng Tu
Impact in
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- Emotion and Mood Recognition
- Artificial Intelligence top 5%
- Sentiment Analysis and Opinion Mining
- Topic Modeling
- Advanced Text Analysis Techniques
- Text and Document Classification Technologies
- Speech and dialogue systems
Papers in
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- Sentiment Analysis and Opinion Mining 15
- Topic Modeling 6
- Text and Document Classification Technologies 4
- Advanced Text Analysis Techniques 1
- Natural Language Processing Techniques 1
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- Emotion and Mood Recognition 11
- Co-authors
- Dazhi Jiang (13 shared papers)Cheng Liu (4 shared papers)Erik Cambria (6 shared papers)Teng Zhou (3 shared papers)Lin Zheng (4 shared papers)Hao Liu (7 shared papers)Dazhi Jiang (2 shared papers)Akhil Garg (1 shared paper)
In The Last Decade
Geng Tu
20 papers receiving 400 citations
Peers
Comparison fields: 5 of 80
- Experimental and Cognitive Psychology 164
- Artificial Intelligence 246
- Human-Computer Interaction 25
- Computer Vision and Pattern Recognition 58
- Signal Processing 28
Countries citing papers authored by Geng Tu
This map shows the geographic impact of Geng Tu'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 Geng Tu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Geng Tu more than expected).
Fields of papers citing papers by Geng Tu
This network shows the impact of papers produced by Geng Tu. 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 Geng Tu. The network helps show where Geng Tu may publish in the future.
Co-authors
The 22 scholars most cited alongside Geng Tu, 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 21 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2022 | 65 | |
| 2 | 2019 | 64 | |
| 3 | 2022 | 57 | |
| 4 | 2020 | 47 | |
| 5 | 2021 | 35 | |
| 6 | 2020 | 29 | |
| 7 | 2024 | 23 | |
| 8 | 2022 | 15 | |
| 9 | 2023 | 14 | |
| 10 | 2021 | 13 | |
| 11 | 2018 | 11 | |
| 12 | 2024 | 7 | |
| 13 | 2024 | 6 | |
| 14 | 2023 | 6 | |
| 15 | 2021 | 6 | |
| 16 | 2023 | 3 | |
| 17 | 2025 | 2 | |
| 18 | 2024 | 2 | |
| 19 | 2024 | 2 | |
| 20 | 2024 | 1 |
About Geng Tu
Geng Tu is a scholar working on Artificial Intelligence, Experimental and Cognitive Psychology, Social Psychology, Computer Vision and Pattern Recognition and Human-Computer Interaction, having authored 21 papers that have together received 408 indexed citations. Recurring topics across this work include Sentiment Analysis and Opinion Mining (15 papers), Emotion and Mood Recognition (11 papers), Topic Modeling (6 papers), Text and Document Classification Technologies (4 papers), Color perception and design (2 papers), Humor Studies and Applications (2 papers), Advanced Text Analysis Techniques (1 paper) and Natural Language Processing Techniques (1 paper). The work is most often cited by research in Experimental and Cognitive Psychology (164 citations), Artificial Intelligence (246 citations), Human-Computer Interaction (25 citations), Computer Vision and Pattern Recognition (58 citations) and Signal Processing (28 citations). Geng Tu has collaborated with scholars based in China, Singapore and Hong Kong. Frequent co-authors include Dazhi Jiang, Cheng Liu, Erik Cambria, Teng Zhou, Lin Zheng, Hao Liu, Dazhi Jiang, Akhil Garg, Liang Gao and Syed Hassan Ahmed. Their work appears in journals such as Knowledge-Based Systems, Information Fusion, Information Sciences, Measurement and International Journal of Machine Learning and Cybernetics.
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.