Daniel Graziotin

2.7k citations
36 papers · 986 · 1 hit paper · h-index 12

Impact in

Papers in

Daniel Graziotin

32 papers receiving 934 citations

Daniel Graziotin's Hit Papers

The evolution of sentiment analysis:a review of research topics, venues, and top cited papers 2018 · 406 citations
4060+2+5Years since publication100200300400

Peers

Daniel Graziotin
Comparison fields: 5 of 109
  • Computer Science Applications 136
  • Information Systems 415
  • Artificial Intelligence 392
  • Communication 59
  • Software 28
Replace Jalal Mahmud with:
Jalal Mahmud United States
Jason D. Baker United States
Álvaro Ortigosa Spain
Nava Tintarev United Kingdom
Mihai Dascălu Romania
Yigal Attali United States
Kathrin Figl Austria
Idris Adjerid United States
H. Ulrich Hoppe Germany
Jill Burstein United States
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Citations per year

Countries citing papers authored by Daniel Graziotin

Since Specialization
Citations

This map shows the geographic impact of Daniel Graziotin'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 Daniel Graziotin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Graziotin more than expected).

Fields of papers citing papers by Daniel Graziotin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel Graziotin. 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 Daniel Graziotin. The network helps show where Daniel Graziotin may publish in the future.

Co-authors

The 25 scholars most cited alongside Daniel Graziotin, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Daniel Graziotin Line = papers co-authored together Daniel Graziotin links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 36 papers — load more, or switch the sort, to bring in the rest.

#Work
1
The evolution of sentiment analysis:a review of research topics, venues, and top cited papers
Hit paper breakdown →
2018406
2 2018132
3 2014122
4 201666
5 201454
6 201537
7 201328
8 201822
9 201722
10 201915
11 202314
12 201312
13 202010
14 20176
15 20135
16 20154
17 20204
18 20233
19 20243
20 20173

About Daniel Graziotin

Daniel Graziotin is a scholar working on Information Systems, Cognitive Neuroscience, Artificial Intelligence, Software and Clinical Psychology, having authored 36 papers that have together received 986 indexed citations. Recurring topics across this work include Software Engineering Techniques and Practices (17 papers), Software Engineering Research (16 papers), Mind wandering and attention (5 papers), Open Source Software Innovations (4 papers), Creativity in Education and Neuroscience (4 papers), Software Reliability and Analysis Research (4 papers), Advanced Software Engineering Methodologies (3 papers) and Resilience and Mental Health (3 papers). The work is most often cited by research in Computer Science Applications (136 citations), Information Systems (415 citations), Artificial Intelligence (392 citations), Communication (59 citations) and Software (28 citations). Daniel Graziotin has collaborated with scholars based in Germany, Italy and United Kingdom. Frequent co-authors include Mika Mäntylä, Miikka Kuutila, Pekka Abrahamsson, Xiaofeng Wang, Fabian Fagerholm, Giuseppe Destefanis, Marco Ortu, Bram Adams, Stefan Wagner and Lutz Prechelt. Their work appears in journals such as PeerJ Computer Science, IEEE Software, Journal of Systems and Software, Information and Software Technology and Empirical Software Engineering.

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.

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