Xin Kang

1.2k citations
68 papers · 769 indexed · h-index 16
Topics
Sentiment Analysis and Opinion Mining (19 papers)Advanced Text Analysis Techniques (15 papers)Text and Document Classification Technologies (12 papers)
Journals
SHILAP Revista de lepidopterologíaMolecular and Cellular BiologyGenetics
Partner nations
JapanChinaCanada

In The Last Decade

Xin Kang

63 papers receiving 743 citations

Peers

Xin Kang
Comparison fields: 5 of 103
  • Artificial Intelligence 239
  • Molecular Biology 156
  • Computer Vision and Pattern Recognition 143
  • Experimental and Cognitive Psychology 90
  • Industrial and Manufacturing Engineering 86
Replace Peng Cao with:
Peng Cao China
Ziming Liu China
Chaoyi Li China
Lijuan Duan China
Mir Mohsen Pedram Iran
Baha Şen Türkiye
Yongli Liu China
Pengbo Zhang China
Yuan-Kai Wang Taiwan
Musa Peker Türkiye
Xin Kang relative to Peng Cao China Peng Cao's profile →
Citations per field
00.5×3.4×
Peng Cao · 1×
Citations per year

Countries citing papers authored by Xin Kang

Since Specialization
Citations

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

Fields of papers citing papers by Xin Kang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Xin Kang

This figure shows the co-authorship network connecting the top 25 collaborators of Xin Kang. A scholar is included among the top collaborators of Xin Kang 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 Xin Kang. Xin Kang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1 1
2 1
3 3
4 4
5 0
6 1
7 0
8 6
9 1
10 30
11 3
12 19
13
A new method of landform element classification based on multi-scale morphology
1
14
KGO at the NTCIR-12 Temporalia Task: Exploring Temporal Information in Search Queries.
1
15 54
16
TUTA1 at the NTCIR-11 Temporalia Task
7
17
Exploring Emotional Words for Chinese Document Chief Emotion Analysis
8
18 2
19 8
20 46

About Xin Kang

Xin Kang is a scholar working on Computational Mathematics, Computer Vision and Pattern Recognition and Experimental and Cognitive Psychology, having authored 68 papers that have together received 769 indexed citations. Recurring topics across this work include Sentiment Analysis and Opinion Mining (19 papers), Advanced Text Analysis Techniques (15 papers) and Text and Document Classification Technologies (12 papers). The work is most often cited by research in Industrial and Manufacturing Engineering (86 citations), Artificial Intelligence (239 citations) and Computer Vision and Pattern Recognition (143 citations). Xin Kang has collaborated with scholars based in Japan, China and Canada. Frequent co-authors include Fuji Ren, Bernard A. Kunz, Hongjun Ni, Jiaqiao Zhang, Changqin Quan, R D Gietz, Xin Lu, Rafael Alonso, Francisco J. López and James D. Fraser. Their work appears in journals such as SHILAP Revista de lepidopterología, Molecular and Cellular Biology and Genetics.

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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