Miha Grćar
Impact in
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- Stock Market Forecasting Methods
- Artificial Intelligence top 5%
- Sentiment Analysis and Opinion Mining
- Advanced Text Analysis Techniques
- Topic Modeling
Papers in
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- Sentiment Analysis and Opinion Mining 4
- Semantic Web and Ontologies 4
- Advanced Text Analysis Techniques 3
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- Web Data Mining and Analysis 4
- Co-authors
- Igor MozetičJasmina SmailovićDarko AleksovskiMartin ŽnidaršičGuido CaldarelliGabriele RancoNada LavračPetra Kralj Novak
- Journals
- PLoS ONE (5 papers)Information Sciences (1 paper)Journal of the Association for Information Systems (1 paper)Scientific Reports (1 paper)Information Processing & Management (1 paper)
- Partner nations
- SloveniaCroatiaUnited States
In The Last Decade
Miha Grćar
23 papers receiving 858 citations
Hit Papers
Peers
Comparison fields: 5 of 90
- Management Science and Operations Research 194
- Artificial Intelligence 468
- Finance 135
- Communication 92
- Statistical and Nonlinear Physics 116
Countries citing papers authored by Miha Grćar
This map shows the geographic impact of Miha Grćar'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 Miha Grćar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Miha Grćar more than expected).
Fields of papers citing papers by Miha Grćar
This network shows the impact of papers produced by Miha Grćar. 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 Miha Grćar. The network helps show where Miha Grćar may publish in the future.
Co-authors
The 25 scholars most cited alongside Miha Grćar, 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 | 2021 | 15 | |
| 2 | 2020 | 14 | |
| 3 | Corpus of Written Standard Slovene Gigafida 2.0 | 2019 | 1 |
| 4 | 2017 | 63 | |
| 5 | 2017 | 25 | |
| 6 | 2016 | 27 | |
| 7 | 2016 | 128 | |
| 8 | Twitter sentiment for 15 European languages | 2016 | 7 |
| 9 | The Effects of Twitter Sentiment on Stock Price Returns Hit paper breakdown → | 2015 | 268 |
| 10 | 2015 | 34 | |
| 11 | 2014 | 57 | |
| 12 | 2014 | 3 | |
| 13 | 2014 | 21 | |
| 14 | ECML-PKDD 2011 Discovery Challenge Overview | 2011 | 10 |
| 15 | 2010 | 8 | |
| 16 | 2008 | 5 | |
| 17 | Data Sparsity Issues in the Collaborative Filtering Framework | 2006 | 2 |
| 18 | 2006 | 6 | |
| 19 | User Profiling for Interest-focused Browsing History | 2005 | 25 |
| 20 | 2005 | 19 |
About Miha Grćar
Miha Grćar is a scholar working on Artificial Intelligence, Information Systems, Statistical and Nonlinear Physics, Finance and Communication, having authored 23 papers that have together received 915 indexed citations. Recurring topics across this work include Sentiment Analysis and Opinion Mining (4 papers), Web Data Mining and Analysis (4 papers), Complex Systems and Time Series Analysis (4 papers), Semantic Web and Ontologies (4 papers), Stock Market Forecasting Methods (3 papers), Complex Network Analysis Techniques (3 papers), Advanced Text Analysis Techniques (3 papers) and Financial Markets and Investment Strategies (3 papers). The work is most often cited by research in Management Science and Operations Research (194 citations), Artificial Intelligence (468 citations), Finance (135 citations), Communication (92 citations) and Statistical and Nonlinear Physics (116 citations). Miha Grćar has collaborated with scholars based in Slovenia, Croatia and United States. Frequent co-authors include Igor Mozetič, Jasmina Smailović, Darko Aleksovski, Martin Žnidaršič, Guido Caldarelli, Gabriele Ranco, Nada Lavrač, Petra Kralj Novak, Dunja Mladenić and Marko Grobelnik. Their work appears in journals such as PLoS ONE, Information Sciences, Journal of the Association for Information Systems, Scientific Reports and Information Processing & Management.
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.