Jens Grivolla
- Transportation top 1%
- Building and Construction top 5%
- Signal Processing top 5%
- Automotive Engineering top 10%
- Artificial Intelligence
- Co-authors
- Joan CodinaRafael E. BanchsAndreas KaltenbrunnerPerfecto HerreraCyril LaurierMarta R. Costa‐jussàCarlos Gerardo Rodriguez-PenagosR. Supyan Sauri
- Topics
- Sentiment Analysis and Opinion Mining (4 papers)Topic Modeling (4 papers)Advanced Text Analysis Techniques (4 papers)
In The Last Decade
Jens Grivolla
13 papers receiving 511 citations
Peers
Comparison fields: 5 of 63
- Transportation 312
- Building and Construction 145
- Signal Processing 130
- Automotive Engineering 121
- Artificial Intelligence 87
Countries citing papers authored by Jens Grivolla
This map shows the geographic impact of Jens Grivolla'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 Jens Grivolla with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jens Grivolla more than expected).
Fields of papers citing papers by Jens Grivolla
This network shows the impact of papers produced by Jens Grivolla. 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 Jens Grivolla. The network helps show where Jens Grivolla may publish in the future.
Co-authorship network of co-authors of Jens Grivolla
This figure shows the co-authorship network connecting the top 25 collaborators of Jens Grivolla. A scholar is included among the top collaborators of Jens Grivolla 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 Jens Grivolla. Jens Grivolla is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | 3 | |
| 3 | 1 | |
| 4 | 4 | |
| 5 | 11 | |
| 6 | FBM: Combining lexicon-based ML and heuristics for Social Media Polarities | 13 |
| 7 | A Hybrid Framework for Scalable Opinion Mining in Social Media: Detecting Polarities and Attitude Targets | 3 |
| 8 | Opinion Mining of Spanish Customer Comments with Non-Expert Annotations on Mechanical Turk | 25 |
| 9 | 334 | |
| 10 | Plagiarism Detection Using Information Retrieval and Similarity Measures Based on Image Processing Techniques - Lab Report for PAN at CLEF 2010. | 3 |
| 11 | Plagiarism detection using information retrieval and similarity measures based on image processing techniques | 4 |
| 12 | Content Analysis in Web 2.0 | 1 |
| 13 | 141 | |
| 14 | Question-answer matching: two complementary methods | 0 |
About Jens Grivolla
Jens Grivolla is a scholar working on Computer Science Applications, Information Systems and Artificial Intelligence, having authored 14 papers that have together received 546 indexed citations. Recurring topics across this work include Sentiment Analysis and Opinion Mining (4 papers), Topic Modeling (4 papers) and Advanced Text Analysis Techniques (4 papers). The work is most often cited by research in Transportation (312 citations), Signal Processing (130 citations) and Building and Construction (145 citations). Jens Grivolla has collaborated with scholars based in Spain, Greece and France. Frequent co-authors include Joan Codina, Rafael E. Banchs, Andreas Kaltenbrunner, Perfecto Herrera, Cyril Laurier, Marta R. Costa‐jussà, Carlos Gerardo Rodriguez-Penagos, R. Supyan Sauri, Jordi Atserias and Toni Badía. Their work appears in journals such as Information Sciences, Electronics and Pervasive and Mobile Computing.
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.