Rui Xia
- Artificial Intelligence top 0.5%
- Information Systems top 2%
- Sociology and Political Science top 10%
- Computer Vision and Pattern Recognition top 10%
- Computer Networks and Communications top 10%
- Topics
- Sentiment Analysis and Opinion Mining (25 papers)Topic Modeling (19 papers)Text and Document Classification Technologies (16 papers)
- Journals
- Optics LettersInternational Journal of Computer VisionIEEE Transactions on Knowledge and Data Engineering
- Partner nations
- ChinaSingaporeUnited States
In The Last Decade
Rui Xia
43 papers receiving 1.6k citations
Peers
Comparison fields: 5 of 102
- Artificial Intelligence 1.4k
- Information Systems 273
- Sociology and Political Science 150
- Computer Vision and Pattern Recognition 138
- Computer Networks and Communications 90
Countries citing papers authored by Rui Xia
This map shows the geographic impact of Rui Xia'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 Rui Xia with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Rui Xia more than expected).
Fields of papers citing papers by Rui Xia
This network shows the impact of papers produced by Rui Xia. 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 Rui Xia. The network helps show where Rui Xia may publish in the future.
Co-authorship network of co-authors of Rui Xia
This figure shows the co-authorship network connecting the top 25 collaborators of Rui Xia. A scholar is included among the top collaborators of Rui Xia 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 Rui Xia. Rui Xia 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 | 1 | |
| 4 | 0 | |
| 5 | 9 | |
| 6 | 11 | |
| 7 | 94 | |
| 8 | 5 | |
| 9 | 28 | |
| 10 | 43 | |
| 11 | 67 | |
| 12 | 128 | |
| 13 | 2 | |
| 14 | 86 | |
| 15 | 16 | |
| 16 | Feature Ensemble Plus Sample Selection: Domain Adaptation for Sentiment Classification (Extended Abstract) | 3 |
| 17 | Feature ensemble plus sample selection: domain adaptation for sentiment classification | 3 |
| 18 | Instance selection and instance weighting for cross-domain sentiment classification via PU learning | 23 |
| 19 | A POS-based Ensemble Model for Cross-domain Sentiment Classification | 30 |
| 20 | Exploring the Use of Word Relation Features for Sentiment Classification | 47 |
About Rui Xia
Rui Xia is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Computer Graphics and Computer-Aided Design, having authored 47 papers that have together received 1.7k indexed citations. Recurring topics across this work include Sentiment Analysis and Opinion Mining (25 papers), Topic Modeling (19 papers) and Text and Document Classification Technologies (16 papers). The work is most often cited by research in Artificial Intelligence (1.4k citations), Information Systems (273 citations) and General Social Sciences (27 citations). Rui Xia has collaborated with scholars based in China, Singapore and United States. Frequent co-authors include Jianfei Yu, Chengqing Zong, Jing Jiang, Feng Xu, Erik Cambria, Xuelei Hu, Yong Qi, Huihui He, Xinyu Dai and Zhen Wu. Their work appears in journals such as Optics Letters, International Journal of Computer Vision and IEEE Transactions on Knowledge and Data Engineering.
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.