Liang-Chih Yu
- Artificial Intelligence top 1%
- Information Systems top 5%
- Computer Science Applications top 2%
- Experimental and Cognitive Psychology top 10%
- Sociology and Political Science top 10%
- Co-authors
- K. Robert LaiJin WangXuejie ZhangJheng-Long WuPei‐Chann ChangChien‐Lung ChanChung‐Hsien WuFong-Lin Jang
- Topics
- Sentiment Analysis and Opinion Mining (30 papers)Topic Modeling (29 papers)Advanced Text Analysis Techniques (29 papers)
In The Last Decade
Liang-Chih Yu
54 papers receiving 1.3k citations
Peers
Comparison fields: 5 of 113
- Artificial Intelligence 924
- Information Systems 166
- Computer Science Applications 142
- Experimental and Cognitive Psychology 122
- Sociology and Political Science 121
Countries citing papers authored by Liang-Chih Yu
This map shows the geographic impact of Liang-Chih Yu'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 Liang-Chih Yu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Liang-Chih Yu more than expected).
Fields of papers citing papers by Liang-Chih Yu
This network shows the impact of papers produced by Liang-Chih Yu. 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 Liang-Chih Yu. The network helps show where Liang-Chih Yu may publish in the future.
Co-authorship network of co-authors of Liang-Chih Yu
This figure shows the co-authorship network connecting the top 25 collaborators of Liang-Chih Yu. A scholar is included among the top collaborators of Liang-Chih Yu 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 Liang-Chih Yu. Liang-Chih Yu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 7 | |
| 2 | 4 | |
| 3 | 2 | |
| 4 | 6 | |
| 5 | 10 | |
| 6 | 9 | |
| 7 | 13 | |
| 8 | 35 | |
| 9 | Sentiment Analysis for Investment Atmosphere Scoring | 1 |
| 10 | 1 | |
| 11 | 100 | |
| 12 | 7 | |
| 13 | 3 | |
| 14 | 2 | |
| 15 | 100 | |
| 16 | Identifying Emotion Labels from Psychiatric Social Texts Using Independent Component Analysis | 8 |
| 17 | 13 | |
| 18 | 15 | |
| 19 | 23 | |
| 20 | Topic Analysis for Psychiatric Document Retrieval | 2 |
About Liang-Chih Yu
Liang-Chih Yu is a scholar working on Artificial Intelligence, Computer Science Applications and Social Psychology, having authored 56 papers that have together received 1.4k indexed citations. Recurring topics across this work include Sentiment Analysis and Opinion Mining (30 papers), Topic Modeling (29 papers) and Advanced Text Analysis Techniques (29 papers). The work is most often cited by research in Artificial Intelligence (924 citations), Computer Science Applications (142 citations) and Experimental and Cognitive Psychology (122 citations). Liang-Chih Yu has collaborated with scholars based in Taiwan, China and Hong Kong. Frequent co-authors include K. Robert Lai, Jin Wang, Xuejie Zhang, Xuejie Zhang, Jheng-Long Wu, Pei‐Chann Chang, Chien‐Lung Chan, Chung‐Hsien Wu, Fong-Lin Jang and Lung‐Hao Lee. Their work appears in journals such as IEEE Access, International Journal of Production Economics and Artificial Intelligence.
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.