Zhijing Jin
- Artificial Intelligence top 2%
- Signal Processing top 5%
- Computer Vision and Pattern Recognition top 10%
- Information Systems top 10%
- Sociology and Political Science
- Co-authors
- Di JinPeter SzolovitsJoey Tianyi ZhouRada MihalceaOlga VechtomovaZhiting HuEnrico SantusMrinmaya Sachan
- Topics
- Topic Modeling (17 papers)Natural Language Processing Techniques (14 papers)Sentiment Analysis and Opinion Mining (4 papers)
- Partner nations
- United StatesSwitzerlandGermany
In The Last Decade
Zhijing Jin
28 papers receiving 855 citations
Hit Papers
Peers
Comparison fields: 5 of 75
- Artificial Intelligence 807
- Signal Processing 140
- Computer Vision and Pattern Recognition 116
- Information Systems 91
- Sociology and Political Science 49
Countries citing papers authored by Zhijing Jin
This map shows the geographic impact of Zhijing Jin'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 Zhijing Jin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Zhijing Jin more than expected).
Fields of papers citing papers by Zhijing Jin
This network shows the impact of papers produced by Zhijing Jin. 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 Zhijing Jin. The network helps show where Zhijing Jin may publish in the future.
Co-authorship network of co-authors of Zhijing Jin
This figure shows the co-authorship network connecting the top 25 collaborators of Zhijing Jin. A scholar is included among the top collaborators of Zhijing Jin 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 Zhijing Jin. Zhijing Jin 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 | 0 | |
| 3 | 8 | |
| 4 | 5 | |
| 5 | 1 | |
| 6 | 0 | |
| 7 | 29 | |
| 8 | 27 | |
| 9 | 2 | |
| 10 | 2 | |
| 11 | 1 | |
| 12 | 11 | |
| 13 | 2 | |
| 14 | 1 | |
| 15 | 15 | |
| 16 | Deep Learning for Text Attribute Transfer: A Survey | 1 |
| 17 | 24 | |
| 18 | 2 | |
| 19 | Is BERT Really Robust? Natural Language Attack on Text Classification and Entailment | 83 |
| 20 | Unsupervised Text Style Transfer via Iterative Matching and Translation. | 5 |
About Zhijing Jin
Zhijing Jin is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Computer Science Applications, having authored 34 papers that have together received 900 indexed citations. Recurring topics across this work include Topic Modeling (17 papers), Natural Language Processing Techniques (14 papers) and Sentiment Analysis and Opinion Mining (4 papers). The work is most often cited by research in Artificial Intelligence (807 citations), Health Informatics (25 citations) and Signal Processing (140 citations). Zhijing Jin has collaborated with scholars based in United States, Switzerland and Germany. Frequent co-authors include Di Jin, Peter Szolovits, Joey Tianyi Zhou, Rada Mihalcea, Olga Vechtomova, Zhiting Hu, Enrico Santus, Mrinmaya Sachan, Bernhard Schoelkopf and Yujie Qian. Their work appears in journals such as PLoS ONE, Journal of Pain and Symptom Management and Computational Linguistics.
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.