Kaiquan Xu
- Artificial Intelligence top 2%
- Information Systems top 2%
- Sociology and Political Science top 5%
- Marketing top 5%
- Information Systems and Management top 5%
- Co-authors
- Stephen Shaoyi LiaoJiexun LiJianshan SunJian MaGang WangRaymond Y.K. LauYuefeng LiYunqing Xia
- Topics
- Sentiment Analysis and Opinion Mining (11 papers)Digital Marketing and Social Media (9 papers)Complex Network Analysis Techniques (6 papers)
- Partner nations
- ChinaHong KongUnited States
In The Last Decade
Kaiquan Xu
29 papers receiving 1.0k citations
Hit Papers
Peers
Comparison fields: 5 of 100
- Artificial Intelligence 593
- Information Systems 366
- Sociology and Political Science 316
- Marketing 145
- Information Systems and Management 83
Countries citing papers authored by Kaiquan Xu
This map shows the geographic impact of Kaiquan Xu'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 Kaiquan Xu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kaiquan Xu more than expected).
Fields of papers citing papers by Kaiquan Xu
This network shows the impact of papers produced by Kaiquan Xu. 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 Kaiquan Xu. The network helps show where Kaiquan Xu may publish in the future.
Co-authorship network of co-authors of Kaiquan Xu
This figure shows the co-authorship network connecting the top 25 collaborators of Kaiquan Xu. A scholar is included among the top collaborators of Kaiquan Xu 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 Kaiquan Xu. Kaiquan Xu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 23 | |
| 3 | 33 | |
| 4 | 63 | |
| 5 | 2 | |
| 6 | MOBILE COMMERCE IN THE NEW TABLET ECONOMY | 15 |
| 7 | Sentiment classification: The contribution of ensemble learningbreakdown → | 288 |
| 8 | 27 | |
| 9 | 20 | |
| 10 | 39 | |
| 11 | Text mining and probabilistic language modeling for online review spam detecting | 12 |
| 12 | An Effective Method of Discovering Target Groups on Social Networking Sites | 2 |
| 13 | 164 | |
| 14 | 27 | |
| 15 | 18 | |
| 16 | An Empirical Study of Online Consumer Review Spam: A Design Science Approach | 11 |
| 17 | 229 | |
| 18 | 4 | |
| 19 | A Framework Design of a Distributed Traffic Information System with Historical Database | 1 |
| 20 | 12 |
About Kaiquan Xu
Kaiquan Xu is a scholar working on Marketing, Statistical and Nonlinear Physics and Artificial Intelligence, having authored 29 papers that have together received 1.1k indexed citations. Recurring topics across this work include Sentiment Analysis and Opinion Mining (11 papers), Digital Marketing and Social Media (9 papers) and Complex Network Analysis Techniques (6 papers). The work is most often cited by research in Artificial Intelligence (593 citations), Information Systems (366 citations) and Marketing (145 citations). Kaiquan Xu has collaborated with scholars based in China, Hong Kong and United States. Frequent co-authors include Stephen Shaoyi Liao, Jiexun Li, Jianshan Sun, Jian Ma, Gang Wang, Raymond Y.K. Lau, Yuefeng Li, Yunqing Xia, Ron Chi-Wai Kwok and Sang Pil Han. Their work appears in journals such as Management Science, Expert Systems with Applications and Marketing Science.
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.