Mohammed Kasri
Impact in
- Artificial Intelligence top 5%
- Sentiment Analysis and Opinion Mining
- Advanced Text Analysis Techniques
- Topic Modeling
- Text and Document Classification Technologies
- Natural Language Processing Techniques
- Information Systems top 5%
- Spam and Phishing Detection
Papers in
-
- Sentiment Analysis and Opinion Mining 5
- Advanced Text Analysis Techniques 5
- Topic Modeling 4
-
- Spam and Phishing Detection 2
- Co-authors
- Abderrahim Beni‐Hssane (7 shared papers)Marouane Birjali (5 shared papers)Khalid El Makkaoui (1 shared paper)Anas El-Ansari (2 shared papers)Mohammed Erritali (1 shared paper)
- Journals
- Knowledge-Based Systems (1 paper)Knowledge and Information Systems (1 paper)Neurocomputing (1 paper)Social Network Analysis and Mining (1 paper)Concurrency and Computation Practice and Experience (1 paper)
- Partner nations
- Morocco
In The Last Decade
Mohammed Kasri
9 papers receiving 589 citations
Mohammed Kasri's Hit Papers
Peers
Comparison fields: 5 of 82
- Artificial Intelligence 466
- Information Systems 131
- Health Informatics 7
- General Social Sciences 11
- Management Science and Operations Research 40
Countries citing papers authored by Mohammed Kasri
This map shows the geographic impact of Mohammed Kasri'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 Mohammed Kasri with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mohammed Kasri more than expected).
Fields of papers citing papers by Mohammed Kasri
This network shows the impact of papers produced by Mohammed Kasri. 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 Mohammed Kasri. The network helps show where Mohammed Kasri may publish in the future.
Co-authors
The 5 scholars most cited alongside Mohammed Kasri, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | A comprehensive survey on sentiment analysis: Approaches, challenges and trends Hit paper breakdown → | 2021 | 558 |
| 2 | 2019 | 19 | |
| 3 | 2020 | 12 | |
| 4 | 2022 | 9 | |
| 5 | 2023 | 7 | |
| 6 | 2025 | 7 | |
| 7 | 2024 | 4 | |
| 8 | 2023 | 2 | |
| 9 | 2025 | 1 |
About Mohammed Kasri
Mohammed Kasri is a scholar working on Artificial Intelligence, Information Systems, Electrical and Electronic Engineering, Computer Networks and Communications and Automotive Engineering, having authored 9 papers that have together received 619 indexed citations. Recurring topics across this work include Sentiment Analysis and Opinion Mining (5 papers), Advanced Text Analysis Techniques (5 papers), Topic Modeling (4 papers), Spam and Phishing Detection (2 papers), Advanced Steganography and Watermarking Techniques (1 paper), IoT and Edge/Fog Computing (1 paper), Computational and Text Analysis Methods (1 paper) and Vehicular Ad Hoc Networks (VANETs) (1 paper). The work is most often cited by research in Artificial Intelligence (466 citations), Information Systems (131 citations), Health Informatics (7 citations), General Social Sciences (11 citations) and Management Science and Operations Research (40 citations). Mohammed Kasri has collaborated with scholars based in Morocco. Frequent co-authors include Abderrahim Beni‐Hssane, Marouane Birjali, Khalid El Makkaoui, Anas El-Ansari and Mohammed Erritali. Their work appears in journals such as Knowledge-Based Systems, Knowledge and Information Systems, Neurocomputing, Social Network Analysis and Mining and Concurrency and Computation Practice and Experience.
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.