Maggie C. M. Lee
- Artificial Intelligence top 10%
- Management Information Systems top 10%
- Management Science and Operations Research top 10%
- Information Systems top 10%
- Industrial and Manufacturing Engineering top 10%
- Co-authors
- Eric W.T. NgaiAriel K.H. LuiHelana ScheepersChester K.M. ToKaren Ka-Leung MoonYoungjoon ChoiYejin Choi
- Topics
- Big Data and Business Intelligence (2 papers)Impact of AI and Big Data on Business and Society (2 papers)Sentiment Analysis and Opinion Mining (2 papers)
- Journals
- International Journal of Production EconomicsInternational Journal of Production ResearchInformation & Management
- Partner nations
- Hong KongAustraliaSouth Korea
In The Last Decade
Maggie C. M. Lee
7 papers receiving 301 citations
Peers
Comparison fields: 5 of 60
- Artificial Intelligence 102
- Management Information Systems 70
- Management Science and Operations Research 53
- Information Systems 47
- Industrial and Manufacturing Engineering 47
Countries citing papers authored by Maggie C. M. Lee
This map shows the geographic impact of Maggie C. M. Lee'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 Maggie C. M. Lee with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Maggie C. M. Lee more than expected).
Fields of papers citing papers by Maggie C. M. Lee
This network shows the impact of papers produced by Maggie C. M. Lee. 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 Maggie C. M. Lee. The network helps show where Maggie C. M. Lee may publish in the future.
Co-authorship network of co-authors of Maggie C. M. Lee
This figure shows the co-authorship network connecting the top 25 collaborators of Maggie C. M. Lee. A scholar is included among the top collaborators of Maggie C. M. Lee 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 Maggie C. M. Lee. Maggie C. M. Lee 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 | 1 | |
| 3 | 68 | |
| 4 | 73 | |
| 5 | 89 | |
| 6 | Multiple-Domain Sentiment Classification for Cantonese Using a Combined Approach | 1 |
| 7 | 34 | |
| 8 | 48 |
About Maggie C. M. Lee
Maggie C. M. Lee is a scholar working on Industrial and Manufacturing Engineering, Management Information Systems and Management Science and Operations Research, having authored 8 papers that have together received 316 indexed citations. Recurring topics across this work include Big Data and Business Intelligence (2 papers), Impact of AI and Big Data on Business and Society (2 papers) and Sentiment Analysis and Opinion Mining (2 papers). The work is most often cited by research in Health Informatics (14 citations), Management Information Systems (70 citations) and Industrial and Manufacturing Engineering (47 citations). Maggie C. M. Lee has collaborated with scholars based in Hong Kong, Australia and South Korea. Frequent co-authors include Eric W.T. Ngai, Ariel K.H. Lui, Helana Scheepers, Chester K.M. To, Karen Ka-Leung Moon, Youngjoon Choi and Yejin Choi. Their work appears in journals such as International Journal of Production Economics, International Journal of Production Research and Information & Management.
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.