Eiji Aramaki
- Artificial Intelligence top 2%
- Molecular Biology
- Sociology and Political Science top 5%
- Epidemiology top 10%
- General Health Professions top 5%
- Co-authors
- Mizuki MoritaShoko WakamiyaKazuhiko OheTomoko OhkumaShuntaro YadaKayo WakiSadao KurohashiYukiko Kawai
- Topics
- Topic Modeling (52 papers)Natural Language Processing Techniques (45 papers)Biomedical Text Mining and Ontologies (41 papers)
- Journals
- SHILAP Revista de lepidopterologíaPLoS ONEScientific Reports
- Partner nations
- JapanUnited StatesPhilippines
In The Last Decade
Eiji Aramaki
135 papers receiving 1.5k citations
Hit Papers
Peers
Comparison fields: 5 of 130
- Artificial Intelligence 694
- Molecular Biology 306
- Sociology and Political Science 278
- Epidemiology 274
- General Health Professions 226
Countries citing papers authored by Eiji Aramaki
This map shows the geographic impact of Eiji Aramaki'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 Eiji Aramaki with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Eiji Aramaki more than expected).
Fields of papers citing papers by Eiji Aramaki
This network shows the impact of papers produced by Eiji Aramaki. 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 Eiji Aramaki. The network helps show where Eiji Aramaki may publish in the future.
Co-authorship network of co-authors of Eiji Aramaki
This figure shows the co-authorship network connecting the top 25 collaborators of Eiji Aramaki. A scholar is included among the top collaborators of Eiji Aramaki 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 Eiji Aramaki. Eiji Aramaki is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 1 | |
| 4 | 0 | |
| 5 | 1 | |
| 6 | 4 | |
| 7 | 3 | |
| 8 | 3 | |
| 9 | 6 | |
| 10 | 2 | |
| 11 | 0 | |
| 12 | 3 | |
| 13 | 2 | |
| 14 | 3 | |
| 15 | A Preliminary Analysis of Offensive Language Transferability from Social Media to Video Live Streaming | 1 |
| 16 | 2 | |
| 17 | 27 | |
| 18 | Overview of the NTCIR-12 MedNLPDoc Task. | 8 |
| 19 | Comparative analysis of sizzle words on the internet | 1 |
| 20 | Analysis of Diffusion of Rumor and CorrectionTweets on the Twitter during Disasters and Normal Situation | 1 |
About Eiji Aramaki
Eiji Aramaki is a scholar working on Toxicology, Artificial Intelligence and Health Informatics, having authored 153 papers that have together received 1.6k indexed citations. Recurring topics across this work include Topic Modeling (52 papers), Natural Language Processing Techniques (45 papers) and Biomedical Text Mining and Ontologies (41 papers). The work is most often cited by research in Health Informatics (53 citations), Toxicology (95 citations) and Artificial Intelligence (694 citations). Eiji Aramaki has collaborated with scholars based in Japan, United States and Philippines. Frequent co-authors include Mizuki Morita, Shoko Wakamiya, Kazuhiko Ohe, Tomoko Ohkuma, Shuntaro Yada, Kayo Waki, Sadao Kurohashi, Yukiko Kawai, Yasuhide Miura and Yoshinobu Kano. Their work appears in journals such as SHILAP Revista de lepidopterología, 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.