Shi Feng
- Artificial Intelligence top 1%
- Information Systems top 2%
- Software top 2%
- Computer Vision and Pattern Recognition top 5%
- Computer Networks and Communications top 10%
- Co-authors
- Daling WangYifei ZhangGábor KarsaiAditya AgrawalXiaocui YangGe YuJonathan SprinkleWei Gao
- Topics
- Topic Modeling (32 papers)Sentiment Analysis and Opinion Mining (26 papers)Text and Document Classification Technologies (15 papers)
- Journals
- IEEE Transactions on Neural Networks and Learning SystemsIEEE Transactions on Knowledge and Data EngineeringKnowledge-Based Systems
- Partner nations
- ChinaUnited StatesAustralia
In The Last Decade
Shi Feng
68 papers receiving 1.0k citations
Peers
Comparison fields: 5 of 82
- Artificial Intelligence 844
- Information Systems 270
- Software 232
- Computer Vision and Pattern Recognition 194
- Computer Networks and Communications 62
Countries citing papers authored by Shi Feng
This map shows the geographic impact of Shi Feng'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 Shi Feng with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shi Feng more than expected).
Fields of papers citing papers by Shi Feng
This network shows the impact of papers produced by Shi Feng. 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 Shi Feng. The network helps show where Shi Feng may publish in the future.
Co-authorship network of co-authors of Shi Feng
This figure shows the co-authorship network connecting the top 25 collaborators of Shi Feng. A scholar is included among the top collaborators of Shi Feng based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Shi Feng. Shi Feng is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 5 | |
| 3 | 0 | |
| 4 | 0 | |
| 5 | 6 | |
| 6 | 1 | |
| 7 | 2 | |
| 8 | 0 | |
| 9 | 3 | |
| 10 | 7 | |
| 11 | 71 | |
| 12 | 31 | |
| 13 | Universal Adversarial Triggers for NLP. | 7 |
| 14 | Improving Attention Modeling with Implicit Distortion and Fertility for Machine Translation | 15 |
| 15 | Automatic assessment of student reading comprehension from short summaries | 3 |
| 16 | Simulating human ratings on word concreteness | 9 |
| 17 | 10 | |
| 18 | A taxi service network equilibrium model with the influenced of demand distribution | 1 |
| 19 | The Statistical Analysis on Monosyllabic Tone of Xi'an Dialect | 2 |
| 20 | Optimization model of planning variable-message board location for parking guidance information system | 1 |
About Shi Feng
Shi Feng is a scholar working on Software, Artificial Intelligence and General Engineering, having authored 81 papers that have together received 1.1k indexed citations. Recurring topics across this work include Topic Modeling (32 papers), Sentiment Analysis and Opinion Mining (26 papers) and Text and Document Classification Technologies (15 papers). The work is most often cited by research in Software (232 citations), Artificial Intelligence (844 citations) and Information Systems (270 citations). Shi Feng has collaborated with scholars based in China, United States and Australia. Frequent co-authors include Daling Wang, Yifei Zhang, Gábor Karsai, Aditya Agrawal, Xiaocui Yang, Ge Yu, Jonathan Sprinkle, Yifei Zhang, Wei Gao and Kaisong Song. Their work appears in journals such as IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Knowledge and Data Engineering and Knowledge-Based Systems.
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.