Junqi Dai
Impact in
- Artificial Intelligence top 5%
- Topic Modeling
- Sentiment Analysis and Opinion Mining
- Natural Language Processing Techniques
- Advanced Text Analysis Techniques
- Text and Document Classification Technologies
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- Data Quality and Management
Papers in
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- Topic Modeling 9
- Natural Language Processing Techniques 6
- Speech and dialogue systems 2
- Sentiment Analysis and Opinion Mining 2
- Explainable Artificial Intelligence (XAI) 2
- Speech Recognition and Synthesis 1
- Advanced Graph Neural Networks 1
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- Multimodal Machine Learning Applications 3
- Co-authors
- Xipeng Qiu (9 shared papers)Hang Yan (3 shared papers)Zheng Zhang (1 shared paper)Qipeng Guo (2 shared papers)Zheng Zhang (2 shared papers)Tao Gui (1 shared paper)Tianxiang Sun (2 shared papers)Pengfei Liu (1 shared paper)
- Journals
- ACM Transactions on Management Information Systems (1 paper)Science China Information Sciences (1 paper)
- Partner nations
- ChinaUnited StatesSingapore
In The Last Decade
Junqi Dai
9 papers receiving 587 citations
Junqi Dai's Hit Papers
Peers
Comparison fields: 5 of 55
- Artificial Intelligence 553
- Management Science and Operations Research 45
- Health Informatics 4
- Information Systems 64
- General Social Sciences 7
Countries citing papers authored by Junqi Dai
This map shows the geographic impact of Junqi Dai'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 Junqi Dai with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Junqi Dai more than expected).
Fields of papers citing papers by Junqi Dai
This network shows the impact of papers produced by Junqi Dai. 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 Junqi Dai. The network helps show where Junqi Dai may publish in the future.
Co-authors
The 25 scholars most cited alongside Junqi Dai, 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 | A Unified Generative Framework for Aspect-based Sentiment Analysis Hit paper breakdown → | 2021 | 205 |
| 2 | 2021 | 192 | |
| 3 | 2021 | 124 | |
| 4 | 2021 | 26 | |
| 5 | 2024 | 21 | |
| 6 | 2024 | 21 | |
| 7 | 2023 | 4 | |
| 8 | 2024 | 4 | |
| 9 | 2024 | 1 | |
| 10 | 2022 | 0 |
About Junqi Dai
Junqi Dai is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging, Infectious Diseases and Organic Chemistry, having authored 10 papers that have together received 598 indexed citations. Recurring topics across this work include Topic Modeling (9 papers), Natural Language Processing Techniques (6 papers), Multimodal Machine Learning Applications (3 papers), Speech and dialogue systems (2 papers), Sentiment Analysis and Opinion Mining (2 papers), Explainable Artificial Intelligence (XAI) (2 papers), Speech Recognition and Synthesis (1 paper) and Advanced Graph Neural Networks (1 paper). The work is most often cited by research in Artificial Intelligence (553 citations), Management Science and Operations Research (45 citations), Health Informatics (4 citations), Information Systems (64 citations) and General Social Sciences (7 citations). Junqi Dai has collaborated with scholars based in China, United States and Singapore. Frequent co-authors include Xipeng Qiu, Hang Yan, Zheng Zhang, Qipeng Guo, Zheng Zhang, Tao Gui, Tianxiang Sun, Pengfei Liu, Pengfei Liu and Xiao Yang. Their work appears in journals such as ACM Transactions on Management Information Systems and Science China Information Sciences.
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.