Linan Zhu

758 citations
33 papers · 462 indexed · 1 hit paper · h-index 10
Topics
Sentiment Analysis and Opinion Mining (11 papers)Topic Modeling (10 papers)Advanced Text Analysis Techniques (8 papers)
Journals
SHILAP Revista de lepidopterologíaExpert Systems with ApplicationsIEEE Access
Partner nations
ChinaHong KongAustralia

In The Last Decade

Linan Zhu

30 papers receiving 436 citations

Hit Papers

Multimodal sentiment analysis based on fusion methods: A ...202320262024202520234080120

Peers

Linan Zhu
Comparison fields: 5 of 83
  • Artificial Intelligence 242
  • Information Systems 78
  • Computer Vision and Pattern Recognition 70
  • Computer Networks and Communications 68
  • Experimental and Cognitive Psychology 68
Replace Rajaram Ganeshan with:
Rajaram Ganeshan India
Andrés Gómez de Silva Garza Mexico
Muhammad Aslam Jarwar South Korea
Tanmay Bhowmik United States
Sushma Jaiswal India
Oleksiy Khriyenko Finland
Gabriel Tamura Colombia
Alan W. Brown United States
Smita Agrawal India
Howard Lei United States
Linan Zhu relative to Rajaram Ganeshan India Rajaram Ganeshan's profile →
Citations per field
00.5×7.1×
Rajaram Ganeshan · 1×
Citations per year

Countries citing papers authored by Linan Zhu

Since Specialization
Citations

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

Fields of papers citing papers by Linan Zhu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Linan Zhu

This figure shows the co-authorship network connecting the top 25 collaborators of Linan Zhu. A scholar is included among the top collaborators of Linan Zhu 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 Linan Zhu. Linan Zhu 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 2
2 3
3 0
4 1
5 0
6 8
7 0
8 2
9 4
10
Multimodal sentiment analysis based on fusion methods: A surveybreakdown →
148
11 5
12 17
13 1
14 45
15 2
16 10
17 11
18 4
19 11
20 4

About Linan Zhu

Linan Zhu is a scholar working on Industrial and Manufacturing Engineering, Artificial Intelligence and General Social Sciences, having authored 33 papers that have together received 462 indexed citations. Recurring topics across this work include Sentiment Analysis and Opinion Mining (11 papers), Topic Modeling (10 papers) and Advanced Text Analysis Techniques (8 papers). The work is most often cited by research in Artificial Intelligence (242 citations), Industrial and Manufacturing Engineering (53 citations) and Experimental and Cognitive Psychology (68 citations). Linan Zhu has collaborated with scholars based in China, Hong Kong and Australia. Frequent co-authors include Xiangjie Kong, Yifei Xu, Chenwei Zhang, Qingshui Li, Yanyan Zhao, Yanwei Zhao, Bing Qin, Guojiang Shen, Zhi Liu and Wanliang Wang. Their work appears in journals such as SHILAP Revista de lepidopterología, Expert Systems with Applications and IEEE Access.

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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