Yung‐Chun Chang
- Artificial Intelligence top 5%
- Sociology and Political Science top 5%
- Molecular Biology
- Strategy and Management top 10%
- Marketing top 5%
- Co-authors
- Chih‐Hao KuMing‐Huei ChenHsiang‐Cheng ChiLiling DengChien‐Hung ChenChang-Shing LeeMei‐Hui WangRichard Tzong‐Han Tsai
- Topics
- Topic Modeling (26 papers)Sentiment Analysis and Opinion Mining (20 papers)Advanced Text Analysis Techniques (17 papers)
- Partner nations
- TaiwanUnited StatesCanada
In The Last Decade
Yung‐Chun Chang
68 papers receiving 959 citations
Peers
Comparison fields: 5 of 123
- Artificial Intelligence 418
- Sociology and Political Science 307
- Molecular Biology 144
- Strategy and Management 141
- Marketing 126
Countries citing papers authored by Yung‐Chun Chang
This map shows the geographic impact of Yung‐Chun Chang'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 Yung‐Chun Chang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yung‐Chun Chang more than expected).
Fields of papers citing papers by Yung‐Chun Chang
This network shows the impact of papers produced by Yung‐Chun Chang. 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 Yung‐Chun Chang. The network helps show where Yung‐Chun Chang may publish in the future.
Co-authorship network of co-authors of Yung‐Chun Chang
This figure shows the co-authorship network connecting the top 25 collaborators of Yung‐Chun Chang. A scholar is included among the top collaborators of Yung‐Chun Chang 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 Yung‐Chun Chang. Yung‐Chun Chang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 0 | |
| 3 | 0 | |
| 4 | 4 | |
| 5 | 14 | |
| 6 | 5 | |
| 7 | 7 | |
| 8 | 14 | |
| 9 | 2 | |
| 10 | Numerical Relation Detection in Financial Tweets using Dependency-aware Deep Neural Network | 1 |
| 11 | 0 | |
| 12 | MONPA: Multi-objective Named-entity and Part-of-speech Annotator for Chinese using Recurrent Neural Network | 4 |
| 13 | Identifying Protein-protein Interactions in Biomedical Literature using Recurrent Neural Networks with Long Short-Term Memory | 24 |
| 14 | 3 | |
| 15 | Sentiment analysis of Chinese microblog message using neural network-based vector representation for measuring Regional prejudice | 3 |
| 16 | 26 | |
| 17 | Semantic Frame-Based Approach for Reader-Emotion Detection | 1 |
| 18 | Semantic Frame-based Statistical Approach for Topic Detection | 1 |
| 19 | 29 | |
| 20 | Robustness Analysis of Adaptive Chinese Input Methods | 0 |
About Yung‐Chun Chang
Yung‐Chun Chang is a scholar working on Health Informatics, Artificial Intelligence and Family Practice, having authored 79 papers that have together received 1000 indexed citations. Recurring topics across this work include Topic Modeling (26 papers), Sentiment Analysis and Opinion Mining (20 papers) and Advanced Text Analysis Techniques (17 papers). The work is most often cited by research in Marketing (126 citations), Health Informatics (18 citations) and Artificial Intelligence (418 citations). Yung‐Chun Chang has collaborated with scholars based in Taiwan, United States and Canada. Frequent co-authors include Chih‐Hao Ku, Ming‐Huei Chen, Hsiang‐Cheng Chi, Liling Deng, Chien‐Hung Chen, Chang-Shing Lee, Mei‐Hui Wang, Richard Tzong‐Han Tsai, Wen−Lian Hsu and Po‐Ting Lai. Their work appears in journals such as Bioinformatics, PLoS ONE and Scientific Reports.
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.