Text and Document Classification Technologies
Text and Document Classification Technologies is a research topic within Artificial Intelligence. Science Explorer counts 23k research works in it since 1954. 22.9% of them reached the world's top 10% most cited for their field and year.
This cluster of papers focuses on the application of machine learning algorithms for multi-label text classification, with an emphasis on techniques such as feature selection, Naive Bayes classifier, K-nearest Neighbor (KNN), hierarchical classification, and support vector machines (SVM). The research covers various aspects of document categorization and information retrieval in the context of text mining and natural language processing.
- Multi-label Learning
- Text Classification
- Feature Selection
- Naive Bayes Classifier
- K-nearest Neighbor (KNN)
- Hierarchical Classification
- Machine Learning Algorithms
- Document Categorization
- Support Vector Machines (SVM)
- Information Retrieval
- Research works
- 23k fractional, since 1954
- In the world top 10%
- 5.2k per year above
- Top-10% rate
- 22.9% share of its works in the world top 10%
- Growth, 2013–17 → 2018–22
- +48% the tick is no change
Which countries lead Text and Document Classification Technologies research?
By volume, China and India publish the most (2.6k and 980 works in 2022–2025).
By volume, 2022–2025
- 1 China 2.6k works
- 2 India 980 works
- 3 United States 400 works
- 4 Indonesia 195 works
- 5 United Kingdom 105 works
- 6 South Korea 103 works
- 7 Japan 102 works
- 8 Türkiye 101 works
- 9 Malaysia 92 works
- 10 Saudi Arabia 90 works
How concentrated that is
The same countries as shares of everything the list above accounts for. A node where two countries do two thirds of the work and one spread evenly across twelve read alike as a ranking and not at all alike here.
Shares of the rows listed above, not of the whole node.
Which institutions lead Text and Document Classification Technologies research?
By volume in 2022–2025, National University of Defense Technology publishes the most Text and Document Classification Technologies research, followed by Harbin Institute of Technology and Xidian University.
By volume, 2022–2025
- 1 National University of Defense TechnologyChina 37 works
- 2 Harbin Institute of TechnologyChina 33 works
- 3 Xidian UniversityChina 31 works
- 4 Southeast UniversityChina 31 works
- 5 Chinese Academy of SciencesChina 31 works
- 6 Guangdong University of TechnologyChina 31 works
- 7 Beijing University of Posts and TelecommunicationsChina 31 works
- 8 University of Electronic Science and Technology of ChinaChina 31 works
- 9 Vellore Institute of Technology UniversityIndia 29 works
- 10 Chongqing University of Posts and TelecommunicationsChina 27 works
Who are the leading researchers in Text and Document Classification Technologies?
The most-cited researchers publishing on Text and Document Classification Technologies include Wei Liu, Chih‐Jen Lin and Thomas S. Huang.
- 1 Wei Liu China 9.7k citations
- 2 Chih‐Jen Lin Taiwan 9.6k citations
- 3 Thomas S. Huang United States 7.4k citations
- 4 Philip S. Yu United States 6.6k citations
- 5 Francisco Herrera Spain 6.5k citations
- 6 Dacheng Tao Australia 6.1k citations
- 7 Witold Pedrycz Canada 5.4k citations
- 8 Lei Zhang Hong Kong 5.3k citations
- 9 Jiawei Han United States 5.2k citations
Ranked by citations received across their whole record, among researchers with at least three works on this topic.
Where is Text and Document Classification Technologies research done?
The largest centres of Text and Document Classification Technologies research in 2022–2025 are Beijing (China), Shanghai (China), Nanjing (China) and Guangzhou (China). Among places with at least 20 works in it, it is an unusually large share of all research in Chandigarh.
Largest cities, 2022–2025
Where it is the local speciality
- ChandigarhIN · 32.8 works5.1×
Location quotient: how much more of its research is in Text and Document Classification Technologies than the world average.
Where is the best place to study Text and Document Classification Technologies?
Among universities, judged by research, Guangdong University of Technology, South China Normal University and Anhui University score highest, combining excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.
One dot per university in the table below. The upper left is the interesting corner: small places doing unusually strong work.
| # | University | Score | Top 10% | Specialisation | Works | Growth |
|---|---|---|---|---|---|---|
| 1 | Guangdong University of TechnologyChina | 65.2 | 17.7% | 9.2× | 31 | +276.2% |
| 2 | South China Normal UniversityChina | 58.0 | 21.6% | 8.7× | 19 | +181.2% |
| 3 | Anhui UniversityChina | 57.1 | 33.2% | 8.3× | 23 | +55.6% |
| 4 | Chongqing University of Posts and TelecommunicationsChina | 56.8 | 16.2% | 15.6× | 27 | +77.3% |
| 5 | Amrita Vishwa VidyapeethamIndia | 56.0 | 22.0% | 6.9× | 22 | +231.6% |
| 6 | National University of Defense TechnologyChina | 55.5 | 28.2% | 7.9× | 37 | +4.7% |
| 7 | National Institute of Technology RaipurIndia | 55.3 | 30.9% | 9.9× | 9 | +566.7% |
| 8 | Xihua UniversityChina | 55.1 | 40.5% | 8.1× | 8 | +233.3% |
| 9 | Minnan Normal UniversityChina | 53.1 | 26.8% | 40.3× | 12 | +122.1% |
| 10 | Xidian UniversityChina | 53.0 | 30.7% | 7.0× | 31 | -0.7% |
Universities only. Score blends excellence (30%), specialisation (25%), size (20%), growth (15%) and international reach (10%), 2015–2022; growth compares 2010–14 with 2015–19.
Is Text and Document Classification Technologies research growing?
Output in 2018–2022 was 48% higher than in 2013–2017, peaking in 2025. The fastest-growing topics are Text and Document Classification Technologies.
The same series as a ribbon — one cell per year, darker for more. The line above answers how much; this answers when.
Which topics inside it are moving
Growth and decline on one axis around a shared zero. Two lists side by side hide the thing that matters: whether the growth dwarfs the decline, or the other way round.