Fei Song

1.2k citations
20 papers · 676 indexed · h-index 9
Topics
Topic Modeling (10 papers)Advanced Text Analysis Techniques (9 papers)Sentiment Analysis and Opinion Mining (7 papers)
Partner nations
CanadaChinaUnited States

In The Last Decade

Fei Song

19 papers receiving 613 citations

Peers

Fei Song
Comparison fields: 5 of 55
  • Artificial Intelligence 547
  • Information Systems 351
  • Signal Processing 62
  • Computer Vision and Pattern Recognition 61
  • Management Science and Operations Research 48
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Fei Song relative to Hema Raghavan United States Hema Raghavan's profile →
Citations per field
00.5×1.5×2.5×
Hema Raghavan · 1×
Citations per year

Countries citing papers authored by Fei Song

Since Specialization
Citations

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

Fields of papers citing papers by Fei Song

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Fei Song

This figure shows the co-authorship network connecting the top 25 collaborators of Fei Song. A scholar is included among the top collaborators of Fei Song 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 Fei Song. Fei Song 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 0
2 3
3 7
4 2
5 10
6 21
7 30
8
Keyphrase Extraction and Grouping Based on Association Rules
3
9
Aspect-Level Sentiment Analysis Based on a Generalized Probabilistic Topic and Syntax Model
12
10 1
11 113
12
Unsupervised Part-of-Speech Tagging in Noisy and Esoteric Domains With a Syntactic-Semantic Bayesian HMM
12
13
Probabilistic Document Modeling for Syntax Removal in Text Summarization
5
14
Feature Selection for Sentiment Analysis Based on Content and Syntax Models
4
15
Web People Search Based on Locality and Relative Similarity Measures
4
16 16
17 8
18
Passage Retrieval by Shrinkage of Language Models.
1
19 100
20 324

About Fei Song

Fei Song is a scholar working on Artificial Intelligence, Management Science and Operations Research and Ecological Modeling, having authored 20 papers that have together received 676 indexed citations. Recurring topics across this work include Topic Modeling (10 papers), Advanced Text Analysis Techniques (9 papers) and Sentiment Analysis and Opinion Mining (7 papers). The work is most often cited by research in Artificial Intelligence (547 citations), Information Systems (351 citations) and Signal Processing (62 citations). Fei Song has collaborated with scholars based in Canada, China and United States. Frequent co-authors include W. Bruce Croft, Selma Ayşe Özel, Rozita Dara, Charlie Obimbo, Michael J. Paul, Xin Li, Jingjing Wang, Robin Cohen, Zhi Sun and Bo Huang. Their work appears in journals such as Decision Support Systems, Journal of Intelligent & Fuzzy Systems and Human-centric Computing and Information Sciences.

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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