Stefan Gindl
- Artificial Intelligence top 5%
- Sociology and Political Science top 10%
- Information Systems top 10%
- Marketing
- Statistical and Nonlinear Physics
- Co-authors
- Arno ScharlAlbert Weichselbraunİrem ÖnderUlrich GunterFabian FischerSilvia MikschKatharina KaiserMarta Sabou
- Topics
- Sentiment Analysis and Opinion Mining (11 papers)Topic Modeling (9 papers)Advanced Text Analysis Techniques (8 papers)
- Partner nations
- AustriaSwitzerlandGreece
In The Last Decade
Stefan Gindl
20 papers receiving 412 citations
Peers
Comparison fields: 5 of 73
- Artificial Intelligence 271
- Sociology and Political Science 121
- Information Systems 82
- Marketing 37
- Statistical and Nonlinear Physics 32
Countries citing papers authored by Stefan Gindl
This map shows the geographic impact of Stefan Gindl'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 Stefan Gindl with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Stefan Gindl more than expected).
Fields of papers citing papers by Stefan Gindl
This network shows the impact of papers produced by Stefan Gindl. 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 Stefan Gindl. The network helps show where Stefan Gindl may publish in the future.
Co-authorship network of co-authors of Stefan Gindl
This figure shows the co-authorship network connecting the top 25 collaborators of Stefan Gindl. A scholar is included among the top collaborators of Stefan Gindl 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 Stefan Gindl. Stefan Gindl 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 | 3 | |
| 4 | 6 | |
| 5 | 6 | |
| 6 | 61 | |
| 7 | 40 | |
| 8 | 40 | |
| 9 | 6 | |
| 10 | 82 | |
| 11 | 4 | |
| 12 | 65 | |
| 13 | Leveraging the Wisdom of the Crowds for the Acquisition of Multilingual Language Resources | 8 |
| 14 | 18 | |
| 15 | Incremental and Scalable Computation of Dynamic Topography Information Landscapes | 3 |
| 16 | Visualizing Contextual and Dynamic Features of Micropost Streams | 11 |
| 17 | 19 | |
| 18 | A Context-Dependent Supervised Learning Approach to Sentiment Detection in Large Textual Databases | 24 |
| 19 | Syntactical negation detection in clinical practice guidelines. | 18 |
| 20 | Negation Detection in Automated Medical Applications A Survey | 6 |
About Stefan Gindl
Stefan Gindl is a scholar working on Artificial Intelligence, Computer Science Applications and Geography, Planning and Development, having authored 22 papers that have together received 440 indexed citations. Recurring topics across this work include Sentiment Analysis and Opinion Mining (11 papers), Topic Modeling (9 papers) and Advanced Text Analysis Techniques (8 papers). The work is most often cited by research in Tourism, Leisure and Hospitality Management (15 citations), Artificial Intelligence (271 citations) and Marketing (37 citations). Stefan Gindl has collaborated with scholars based in Austria, Switzerland and Greece. Frequent co-authors include Arno Scharl, Albert Weichselbraun, İrem Önder, Ulrich Gunter, Fabian Fischer, Silvia Miksch, Katharina Kaiser, Marta Sabou, Josef Ruppenhofer and Ulli Waltinger. Their work appears in journals such as Journal of Travel Research, Knowledge-Based Systems and IEEE Intelligent Systems.
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.