Liang Yang

2.8k citations
96 papers · 1.7k indexed · 2 hit papers · h-index 22
Topics
Topic Modeling (34 papers)Sentiment Analysis and Opinion Mining (27 papers)Advanced Text Analysis Techniques (18 papers)
Partner nations
ChinaUnited StatesIndia

In The Last Decade

Liang Yang

89 papers receiving 1.6k citations

Hit Papers

Detection of Depression-Related Posts in Reddit Social Me...20192026202120232019202550100150200250

Peers

Liang Yang
Comparison fields: 5 of 122
  • Artificial Intelligence 1.0k
  • Social Psychology 506
  • Information Systems 253
  • Applied Psychology 211
  • Experimental and Cognitive Psychology 196
Replace Krishnaprasad Thirunarayan with:
Krishnaprasad Thirunarayan United States
Shaoxiong Ji Finland
Tibor Bosse Netherlands
Wessel Kraaij Netherlands
Derwin Suhartono Indonesia
Giuseppe Riccardi Italy
Muhammad Ashad Kabir Australia
Hafiz Farooq Ahmad Pakistan
Navonil Majumder Singapore
Mohammed Ali Al-Garadi United States
Liang Yang relative to Krishnaprasad Thirunarayan United States Krishnaprasad Thirunarayan's profile →
Citations per field
00.5×1.5×2.5×
Krishnaprasad Thirunarayan · 1×
Citations per year

Countries citing papers authored by Liang Yang

Since Specialization
Citations

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

Fields of papers citing papers by Liang Yang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Liang Yang

This figure shows the co-authorship network connecting the top 25 collaborators of Liang Yang. A scholar is included among the top collaborators of Liang Yang 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 Liang Yang. Liang Yang 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
Machine learning-enhanced flavoromics: Identifying key aroma compounds and predicting sensory quality in sauce-flavor baijiubreakdown →
19
2 0
3 0
4 1
5 0
6 1
7 7
8 22
9 22
10 4
11 18
12 44
13 18
14 59
15
Detection of Depression-Related Posts in Reddit Social Media Forumbreakdown →
253
16 43
17 9
18 7
19 122
20 13

About Liang Yang

Liang Yang is a scholar working on Artificial Intelligence, Computational Mathematics and Social Psychology, having authored 96 papers that have together received 1.7k indexed citations. Recurring topics across this work include Topic Modeling (34 papers), Sentiment Analysis and Opinion Mining (27 papers) and Advanced Text Analysis Techniques (18 papers). The work is most often cited by research in Applied Psychology (211 citations), Artificial Intelligence (1.0k citations) and Social Psychology (506 citations). Liang Yang has collaborated with scholars based in China, United States and India. Frequent co-authors include Hongfei Lin, Bo Xu, Bo Xu, Yufeng Diao, Kan Xu, Xiaochao Fan, Shaowu Zhang, Zhihao Yang, Yijia Zhang and Yuan Lin. Their work appears in journals such as Food Chemistry, IEEE Access and BMC Bioinformatics.

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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