Da Yin
- Artificial Intelligence top 5%
- Topic Modeling 7
- Advanced Text Analysis Techniques 4
- Sentiment Analysis and Opinion Mining 3
- Metaheuristic Optimization Algorithms Research 2
- Natural Language Processing Techniques 2
- Advanced Graph Neural Networks 2
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- Multimodal Machine Learning Applications 3
- General Social Sciences top 10%
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- Advanced Algorithms and Applications 2
- Co-authors
- Kai-Wei ChangLiunian Harold LiMark YatskarCho‐Jui HsiehYang LiuRahul JhaTao YuYansong Feng
- Journals
- IEEE Transactions on Knowledge and Data Engineering (1 paper)International Journal of Computer Applications in Technology (1 paper)Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (1 paper)
- Partner nations
- ChinaUnited StatesIsrael
In The Last Decade
Da Yin
12 papers receiving 264 citations
Peers
Comparison fields: 5 of 41
- Artificial Intelligence 245
- Computer Vision and Pattern Recognition 79
- Health Informatics 4
- General Social Sciences 8
- Management Science and Operations Research 13
Countries citing papers authored by Da Yin
This map shows the geographic impact of Da Yin'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 Da Yin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Da Yin more than expected).
Fields of papers citing papers by Da Yin
This network shows the impact of papers produced by Da Yin. 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 Da Yin. The network helps show where Da Yin may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Da Yin, 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 | 2024 | 0 | |
| 2 | 2022 | 19 | |
| 3 | 2022 | 18 | |
| 4 | 2021 | 102 | |
| 5 | 2021 | 21 | |
| 6 | 2021 | 5 | |
| 7 | 2021 | 7 | |
| 8 | 2021 | 20 | |
| 9 | 2020 | 73 | |
| 10 | 2020 | 2 | |
| 11 | 2019 | 3 | |
| 12 | 2019 | 1 | |
| 13 | 2013 | 5 | |
| 14 | 2009 | 1 |
About Da Yin
Da Yin is a scholar working on Artificial Intelligence, General Social Sciences and Computer Vision and Pattern Recognition, having authored 14 papers that have together received 277 indexed citations. Recurring topics across this work include Topic Modeling (7 papers), Advanced Text Analysis Techniques (4 papers), Sentiment Analysis and Opinion Mining (3 papers), Multimodal Machine Learning Applications (3 papers), Metaheuristic Optimization Algorithms Research (2 papers), Advanced Algorithms and Applications (2 papers), Natural Language Processing Techniques (2 papers) and Advanced Graph Neural Networks (2 papers). The work is most often cited by research in Artificial Intelligence (245 citations), Computer Vision and Pattern Recognition (79 citations) and Health Informatics (4 citations). Da Yin has collaborated with scholars based in China, United States and Israel. Frequent co-authors include Kai-Wei Chang, Liunian Harold Li, Mark Yatskar, Cho‐Jui Hsieh, Yang Liu, Rahul Jha, Tao Yu, Yansong Feng, Ahmed Hassan Awadallah and Xipeng Qiu. Their work appears in journals such as IEEE Transactions on Knowledge and Data Engineering, International Journal of Computer Applications in Technology, Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, arXiv (Cornell University) and Proceedings of the AAAI Conference on Artificial Intelligence.
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.