Countries citing papers authored by Michał Ptaszyński
Since
Specialization
Citations
This map shows the geographic impact of Michał Ptaszyński'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 Michał Ptaszyński with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michał Ptaszyński more than expected).
Fields of papers citing papers by Michał Ptaszyński
This network shows the impact of papers produced by Michał Ptaszyński. 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 Michał Ptaszyński. The network helps show where Michał Ptaszyński may publish in the future.
Co-authorship network of co-authors of Michał Ptaszyński
This figure shows the co-authorship network connecting the top 25 collaborators of Michał Ptaszyński.
A scholar is included among the top collaborators of Michał Ptaszyński 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 Michał Ptaszyński. Michał Ptaszyński is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Ptaszyński, Michał, et al.. (2020). A Study in Practical Solutions to Sarcasm Detection with Machine Learning and Knowledge Engineering Techniques..2 indexed citations
8.
Rzepka, Rafał, et al.. (2019). A Novel Machine Learning-based Sentiment Analysis Method for Chinese Social Media Considering Chinese Slang Lexicon and Emoticons.. National Conference on Artificial Intelligence. 88–103.4 indexed citations
9.
Nakajima, Yoko, et al.. (2018). Future Reference Sentence Extraction in Support of Future Event Prediction. 9(1). 27–41.2 indexed citations
10.
Ptaszyński, Michał, et al.. (2017). Learning Deep on Cyberbullying is Always Better Than Brute Force.. International Joint Conference on Artificial Intelligence. 3–10.20 indexed citations
11.
Masui, Fumito, et al.. (2016). Statistical Analysis of Automatic Seed Word Acquisition to Improve Harmful Expression Extraction in Cyberbullying Detection. SHILAP Revista de lepidopterología.5 indexed citations
12.
Ptaszyński, Michał, et al.. (2016). Modeling Learning motivation of students based on analysis of class evaluation questionnaire. RPK (Politechniki Krakowskiej). 2015. 193–201.1 indexed citations
13.
Ptaszyński, Michał, et al.. (2016). Recognizing and Converting Cockney Rhyming Slang for Cyberbullying and Crime Detection. Hokkaido University Collection of Scholarly and Academic Papers (Hokkaido University).3 indexed citations
14.
Ptaszyński, Michał, et al.. (2015). Brute force works best against bullying. CEUR Workshop Proceedings. 1440. 28–29.4 indexed citations
15.
Ptaszyński, Michał, et al.. (2014). Using Time Periods Comparison for Eliminating Chronological Discrepancies between Question and Answer Candidates at QALab NTCIR11 Task. NTCIR.
16.
Dybała, Paweł, Michał Ptaszyński, & Rafał Rzepka. (2012). Beyond Conventional Recognition: Concept of a Conversational System Utilizing Metaphor Misunderstanding as a Source of Humor (人工知能学会全国大会(第26回)文化,科学技術と未来) -- (International Organized Session「Alan Turing Year Special Session on AI Research That Can Change The World」). 26. 1–10.
17.
Ptaszyński, Michał, et al.. (2012). Automatically Annotating A Five-Billion-Word Corpus of Japanese Blogs for Affect and Sentiment Analysis. Meeting of the Association for Computational Linguistics. 89–98.5 indexed citations
Dybała, Paweł, Michał Ptaszyński, Rafał Rzepka, & Kenji Araki. (2010). Extending the Chain: Humor and Emotions in Human Computer Interaction. Hokkaido University Collection of Scholarly and Academic Papers (Hokkaido University). 1(3). 116–125.1 indexed citations
20.
Ptaszyński, Michał, Paweł Dybała, & Rafał Rzepka. (2008). Double Standpoint Evaluation Method for Affect Analysis Systems. 22. 1–4.6 indexed citations
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.