Chihli Hung

1.1k citations
44 papers · 836 · h-index 13

Impact in

    • Sentiment Analysis and Opinion Mining
    • Advanced Text Analysis Techniques
    • Imbalanced Data Classification Techniques
    • Text and Document Classification Technologies
    • Topic Modeling
  • Marketing top 10%

Papers in

Chihli Hung

42 papers receiving 766 citations

Peers

Chihli Hung
Comparison fields: 5 of 81
  • Artificial Intelligence 534
  • Marketing 100
  • Accounting 122
  • Information Systems 229
  • Computer Vision and Pattern Recognition 147
Replace Saeedeh Momtazi with:
Saeedeh Momtazi Iran
Indranil Bose Hong Kong
Yoon‐Joo Park South Korea
Ahmad Hawalah United Kingdom
Juan A. Recio-Garcí­a Spain
Karel Dejaeger Belgium
Much Aziz Muslim Indonesia
Yi Liang United States
Kaiquan Xu China
Chihli Hung relative to Saeedeh Momtazi Iran Saeedeh Momtazi's profile →
Citations per field
00.5×3.7×
Saeedeh Momtazi · 1×
Citations per year

Countries citing papers authored by Chihli Hung

Since Specialization
Citations

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

Fields of papers citing papers by Chihli Hung

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 13 scholars most cited alongside Chihli Hung, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Chihli Hung Line = papers co-authored together Chihli Hung links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 44 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2008115
2 2011111
3 201395
4 200689
5 201667
6 201048
7 201746
8 200435
9 201432
10 200426
11 200519
12 200216
13 201412
14 200811
15 201010
16 201210
17 20089
18 20178
19 20208
20 20068

About Chihli Hung

Chihli Hung is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems, Signal Processing and Sociology and Political Science, having authored 44 papers that have together received 836 indexed citations. Recurring topics across this work include Advanced Text Analysis Techniques (16 papers), Neural Networks and Applications (10 papers), Advanced Clustering Algorithms Research (7 papers), Sentiment Analysis and Opinion Mining (6 papers), Face and Expression Recognition (5 papers), Digital Marketing and Social Media (5 papers), Time Series Analysis and Forecasting (5 papers) and Stock Market Forecasting Methods (4 papers). The work is most often cited by research in Artificial Intelligence (534 citations), Marketing (100 citations), Accounting (122 citations), Information Systems (229 citations) and Computer Vision and Pattern Recognition (147 citations). Chihli Hung has collaborated with scholars based in Taiwan, United Kingdom and Germany. Frequent co-authors include Chih‐Fong Tsai, Jinghong Chen, Stefan Wermter, Peter G. R. Smith, Yu‐Liang Chi, Szu‐Yin Lin, Wanrong Wu, Wei‐Chao Lin, Peter Smith and Shin‐Yuan Hung. Their work appears in journals such as Expert Systems with Applications, IEEE Intelligent Systems, The Electronic Library, Knowledge-Based Systems and Language Resources and Evaluation.

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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