Long‐Sheng Chen
- Artificial Intelligence top 2%
- Sentiment Analysis and Opinion Mining 17
- Imbalanced Data Classification Techniques 11
- Advanced Text Analysis Techniques 7
- Marketing top 5%
- Information Systems top 2%
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- Online Learning and Analytics 8
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- Digital Marketing and Social Media 25
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- Customer Service Quality and Loyalty 15
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- Technology Adoption and User Behaviour 14
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- Fault Detection and Control Systems 7
Long‐Sheng Chen
91 papers receiving 1.3k citations
Peers
Comparison fields: 5 of 130
- Artificial Intelligence 504
- Marketing 143
- Information Systems 306
- Statistics, Probability and Uncertainty 87
- Computer Science Applications 65
Countries citing papers authored by Long‐Sheng Chen
This map shows the geographic impact of Long‐Sheng Chen'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 Long‐Sheng Chen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Long‐Sheng Chen more than expected).
Fields of papers citing papers by Long‐Sheng Chen
This network shows the impact of papers produced by Long‐Sheng Chen. 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 Long‐Sheng Chen. The network helps show where Long‐Sheng Chen may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Long‐Sheng Chen, 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 | 2025 | 0 | |
| 2 | 2024 | 1 | |
| 3 | 2024 | 6 | |
| 4 | 2024 | 5 | |
| 5 | 2023 | 17 | |
| 6 | 2023 | 4 | |
| 7 | 2023 | 2 | |
| 8 | 2023 | 0 | |
| 9 | 2023 | 7 | |
| 10 | 2023 | 7 | |
| 11 | 2023 | 3 | |
| 12 | 2023 | 3 | |
| 13 | 2023 | 11 | |
| 14 | 2022 | 3 | |
| 15 | 2021 | 10 | |
| 16 | 2020 | 7 | |
| 17 | 2020 | 15 | |
| 18 | 2020 | 42 | |
| 19 | 2015 | 5 | |
| 20 | 2013 | 1 |
About Long‐Sheng Chen
Long‐Sheng Chen is a scholar working on Information Systems and Management, Organizational Behavior and Human Resource Management, Computer Science Applications, Management Information Systems and Artificial Intelligence, having authored 110 papers that have together received 1.4k indexed citations. Recurring topics across this work include Digital Marketing and Social Media (25 papers), Sentiment Analysis and Opinion Mining (17 papers), Customer Service Quality and Loyalty (15 papers), Technology Adoption and User Behaviour (14 papers), Imbalanced Data Classification Techniques (11 papers), Online Learning and Analytics (8 papers), Advanced Text Analysis Techniques (7 papers) and Fault Detection and Control Systems (7 papers). The work is most often cited by research in Artificial Intelligence (504 citations), Marketing (143 citations), Information Systems (306 citations), Statistics, Probability and Uncertainty (87 citations) and Computer Science Applications (65 citations). Long‐Sheng Chen has collaborated with scholars based in Taiwan, Vietnam and Japan. Frequent co-authors include Mu‐Chen Chen, Chun-Chin Hsu, Cheng-Hsiang Liu, Chao‐Ton Su, Kai-Ying Chen, Jing–Rong Chang, Der‐Chiang Li, Mu‐Yen Chen, Yuehwern Yih and Rung-Ching Chen. Their work appears in journals such as Big Data and Cognitive Computing, Multimedia Tools and Applications, Information Sciences, Journal of Ambient Intelligence and Humanized Computing and International Journal of Production Research.
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.