Debanjan Mahata
- Artificial Intelligence top 2%
- Information Systems top 5%
- Social Psychology top 10%
- Sociology and Political Science
- Computer Vision and Pattern Recognition
- Co-authors
- Rajiv Ratn ShahRamit SawhneyPuneet MathurRoger ZimmermannArijit ChowdhuryYaman KumarJing JiangHaimin Zhang
- Topics
- Sentiment Analysis and Opinion Mining (13 papers)Topic Modeling (12 papers)Advanced Text Analysis Techniques (8 papers)
- Journals
- Journal of the American Medical Informatics AssociationIEEE Intelligent SystemsLanguage Resources and Evaluation
- Partner nations
- IndiaUnited StatesSingapore
In The Last Decade
Debanjan Mahata
37 papers receiving 608 citations
Peers
Comparison fields: 5 of 71
- Artificial Intelligence 504
- Information Systems 138
- Social Psychology 87
- Sociology and Political Science 66
- Computer Vision and Pattern Recognition 55
Countries citing papers authored by Debanjan Mahata
This map shows the geographic impact of Debanjan Mahata'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 Debanjan Mahata with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Debanjan Mahata more than expected).
Fields of papers citing papers by Debanjan Mahata
This network shows the impact of papers produced by Debanjan Mahata. 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 Debanjan Mahata. The network helps show where Debanjan Mahata may publish in the future.
Co-authorship network of co-authors of Debanjan Mahata
This figure shows the co-authorship network connecting the top 25 collaborators of Debanjan Mahata. A scholar is included among the top collaborators of Debanjan Mahata 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 Debanjan Mahata. Debanjan Mahata 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 | 2 | |
| 3 | An Annotated Dataset of Discourse Modes in Hindi Stories | 3 |
| 4 | 19 | |
| 5 | 4 | |
| 6 | 1 | |
| 7 | 28 | |
| 8 | 35 | |
| 9 | 5 | |
| 10 | 24 | |
| 11 | 7 | |
| 12 | 61 | |
| 13 | 29 | |
| 14 | A Framework for Collecting, Extracting and Managing Event Identity Information from Twitter. | 2 |
| 15 | 11 | |
| 16 | 3 | |
| 17 | 8 | |
| 18 | 2 | |
| 19 | 6 | |
| 20 | 16 |
About Debanjan Mahata
Debanjan Mahata is a scholar working on Artificial Intelligence, Information Systems and Communication, having authored 38 papers that have together received 661 indexed citations. Recurring topics across this work include Sentiment Analysis and Opinion Mining (13 papers), Topic Modeling (12 papers) and Advanced Text Analysis Techniques (8 papers). The work is most often cited by research in Artificial Intelligence (504 citations), Information Systems (138 citations) and Applied Psychology (31 citations). Debanjan Mahata has collaborated with scholars based in India, United States and Singapore. Frequent co-authors include Rajiv Ratn Shah, Ramit Sawhney, Puneet Mathur, Roger Zimmermann, Arijit Chowdhury, Yaman Kumar, Jing Jiang, Haimin Zhang, Vivek Kumar Singh and Huan Liu. Their work appears in journals such as Journal of the American Medical Informatics Association, IEEE Intelligent Systems and Language Resources and Evaluation.
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.