Hang Su
Impact in
- Artificial Intelligence top 1%
- Sentiment Analysis and Opinion Mining
- Advanced Text Analysis Techniques
- Topic Modeling
- Text and Document Classification Technologies
- Information Systems top 5%
- Web Data Mining and Analysis
- Spam and Phishing Detection
Papers in
-
- Topic Modeling 6
- Sentiment Analysis and Opinion Mining 3
- Natural Language Processing Techniques 3
- Advanced Text Analysis Techniques 2
- Semantic Web and Ontologies 2
- Text and Document Classification Technologies 2
- Co-authors
- ChengXiang Zhai (1 shared paper)Xu Ling (1 shared paper)Qiaozhu Mei (1 shared paper)Bin Liang (2 shared papers)Ruifeng Xu (2 shared papers)Lin Gui (2 shared papers)Erik Cambria (1 shared paper)Wei Li (1 shared paper)
- Journals
- Frontiers in Psychology (2 papers)Sustainability (1 paper)Connection Science (1 paper)Knowledge-Based Systems (1 paper)OCEANS 2022, Hampton Roads (1 paper)
- Partner nations
- ChinaUnited KingdomSingapore
In The Last Decade
Hang Su
9 papers receiving 895 citations
Hang Su's Hit Papers
Peers
Comparison fields: 5 of 67
- Artificial Intelligence 856
- Information Systems 185
- General Social Sciences 24
- Statistical and Nonlinear Physics 69
- Management Science and Operations Research 37
Countries citing papers authored by Hang Su
This map shows the geographic impact of Hang Su'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 Hang Su with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hang Su more than expected).
Fields of papers citing papers by Hang Su
This network shows the impact of papers produced by Hang Su. 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 Hang Su. The network helps show where Hang Su may publish in the future.
Co-authors
The 25 scholars most cited alongside Hang Su, 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 | Topic sentiment mixture Hit paper breakdown → | 2007 | 533 |
| 2 | Aspect-based sentiment analysis via affective knowledge enhanced graph convolutional networks Hit paper breakdown → | 2021 | 384 |
| 3 | 2021 | 15 | |
| 4 | 2024 | 9 | |
| 5 | 2022 | 9 | |
| 6 | 2023 | 3 | |
| 7 | 2023 | 2 | |
| 8 | 2024 | 1 | |
| 9 | 2025 | 1 | |
| 10 | 2024 | 0 | |
| 11 | 2024 | 0 | |
| 12 | 2025 | 0 |
About Hang Su
Hang Su is a scholar working on Artificial Intelligence, Molecular Biology, Social Psychology, Information Systems and Computer Vision and Pattern Recognition, having authored 12 papers that have together received 957 indexed citations. Recurring topics across this work include Topic Modeling (6 papers), Sentiment Analysis and Opinion Mining (3 papers), Natural Language Processing Techniques (3 papers), Advanced Text Analysis Techniques (2 papers), Semantic Web and Ontologies (2 papers), Text and Document Classification Technologies (2 papers), Web Data Mining and Analysis (1 paper) and Remote Sensing in Agriculture (1 paper). The work is most often cited by research in Artificial Intelligence (856 citations), Information Systems (185 citations), General Social Sciences (24 citations), Statistical and Nonlinear Physics (69 citations) and Management Science and Operations Research (37 citations). Hang Su has collaborated with scholars based in China, United Kingdom and Singapore. Frequent co-authors include ChengXiang Zhai, Xu Ling, Qiaozhu Mei, Bin Liang, Ruifeng Xu, Lin Gui, Erik Cambria, Wei Li, Min Yang and Song Feng. Their work appears in journals such as Frontiers in Psychology, Sustainability, Connection Science, Knowledge-Based Systems and OCEANS 2022, Hampton Roads.
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.