Parth Patwa
- Artificial Intelligence top 10%
- Biomedical Engineering
- Information Systems
- Sociology and Political Science
- Cardiology and Cardiovascular Medicine
- Co-authors
- Amitava DasSrinivas PyklThamar SolorioBjörn GambäckTanmoy ChakrabortySudipta KarGustavo AguilarPrerana Mukherjee
- Topics
- Sentiment Analysis and Opinion Mining (4 papers)Hate Speech and Cyberbullying Detection (4 papers)Topic Modeling (2 papers)
- Journals
- AI MagazineSN Computer Science2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Partner nations
- IndiaUnited StatesNorway
In The Last Decade
Parth Patwa
8 papers receiving 136 citations
Peers
Comparison fields: 5 of 34
- Artificial Intelligence 116
- Biomedical Engineering 22
- Information Systems 19
- Sociology and Political Science 18
- Cardiology and Cardiovascular Medicine 16
Countries citing papers authored by Parth Patwa
This map shows the geographic impact of Parth Patwa'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 Parth Patwa with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Parth Patwa more than expected).
Fields of papers citing papers by Parth Patwa
This network shows the impact of papers produced by Parth Patwa. 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 Parth Patwa. The network helps show where Parth Patwa may publish in the future.
Co-authorship network of co-authors of Parth Patwa
This figure shows the co-authorship network connecting the top 25 collaborators of Parth Patwa. A scholar is included among the top collaborators of Parth Patwa 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 Parth Patwa. Parth Patwa 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 | 24 | |
| 3 | 3 | |
| 4 | 0 | |
| 5 | 0 | |
| 6 | 0 | |
| 7 | 0 | |
| 8 | 1 | |
| 9 | Bitions@DravidianLangTech-EACL2021: Ensemble of Multilingual Language Models with Pseudo Labeling for offence Detection in Dravidian Languages | 1 |
| 10 | 69 | |
| 11 | Aggression and Misogyny Detection using BERT: A Multi-Task Approach | 38 |
| 12 | 7 |
About Parth Patwa
Parth Patwa is a scholar working on Human-Computer Interaction, Artificial Intelligence and Radiology, Nuclear Medicine and Imaging, having authored 12 papers that have together received 150 indexed citations. Recurring topics across this work include Sentiment Analysis and Opinion Mining (4 papers), Hate Speech and Cyberbullying Detection (4 papers) and Topic Modeling (2 papers). The work is most often cited by research in Artificial Intelligence (116 citations), Human-Computer Interaction (6 citations) and Signal Processing (11 citations). Parth Patwa has collaborated with scholars based in India, United States and Norway. Frequent co-authors include Amitava Das, Srinivas Pykl, Thamar Solorio, Björn Gambäck, Tanmoy Chakraborty, Sudipta Kar, Gustavo Aguilar, Prerana Mukherjee, Achuta Kadambi and Laleh Jalilian. Their work appears in journals such as AI Magazine, SN Computer Science and 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
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.