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Topic · Artificial Intelligence

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. 9.9% 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%
2.2k
per year above
Top-10% rate
9.9%
share of its works in the world top 10%
Growth, 2018-21 → 2022-25
+27%
the tick is no change
Largest producer
China
54% of the 10 largest, 2022-now
Text and Document Classification Technologies

Which countries lead Text and Document Classification Technologies research?

By volume, China and India publish the most (2.9k and 1.1k works in 2022-now).

By volume, 2022-now

  1. 1 China 2.9k works
  2. 2 India 1.1k works
  3. 3 United States 454 works
  4. 4 Indonesia 219 works
  5. 5 United Kingdom 116 works
  6. 6 Japan 112 works
  7. 7 South Korea 111 works
  8. 8 Türkiye 109 works
  9. 9 Saudi Arabia 100 works
  10. 10 Malaysia 97 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.

China: 54.1%India: 21.1%United States: 8.5%Indonesia: 4.1%6 others listed: 12.1%54%largest
China2,882 · 54.1%India1,126 · 21.1%United States454 · 8.5%Indonesia219 · 4.1%6 others listed646 · 12.1%

Shares of the rows listed above, not of the whole node.

Which institutions lead Text and Document Classification Technologies research?

By volume in 2022-now, Chinese Academy of Sciences publishes the most Text and Document Classification Technologies research, followed by National University of Defense Technology and Harbin Institute of Technology.

By volume, 2022-now

  1. 1 Chinese Academy of SciencesChina 85 works
  2. 2 National University of Defense TechnologyChina 41 works
  3. 3 Harbin Institute of TechnologyChina 36 works
  4. 4 Xidian UniversityChina 34 works
  5. 5 SRM Institute of Science and TechnologyIndia 34 works
  6. 6 Southeast UniversityChina 33 works
  7. 7 Beijing University of Posts and TelecommunicationsChina 33 works
  8. 8 Guangdong University of TechnologyChina 32 works
  9. 9 University of Electronic Science and Technology of ChinaChina 32 works
  10. 10 Vellore Institute of Technology UniversityIndia 32 works

Who are the leading researchers in Text and Document Classification Technologies?

The most-cited researchers publishing on Text and Document Classification Technologies include Yoshua Bengio, Trevor Hastie and Xiaogang Wang.

  1. 1 Yoshua Bengio Canada 476k citations
  2. 2 Trevor Hastie United States 301k citations
  3. 3 Xiaogang Wang China 260k citations
  4. 4 Yann LeCun United States 256k citations
  5. 5 Andrew Zisserman United Kingdom 255k citations
  6. 6 James Thomas United Kingdom 242k citations
  7. 7 Vladimir N. Vapnik United States 227k citations
  8. 8 Li Fei-Fei United States 225k citations
  9. 9 Kai You Wang China 224k citations
  10. 10 Lei Jiang China 214k citations

Ranked by citations received across their whole record, among researchers with at least three works on this topic.

What are the most cited papers in Text and Document Classification Technologies?

The most cited paper in Text and Document Classification Technologies is "Glove: Global Vectors for Word Representation" (Jeffrey Pennington, Richard Socher, Christopher D. Manning, 2014), with 34,197 citations. Next come "SMOTE: Synthetic Minority Over-sampling Technique" (32,759) and "Comparison of Convenience Sampling and Purposive Sampling" (11,287).

Most cited papers
#PaperReleasedCitations
1 Glove: Global Vectors for Word Representation Jeffrey Pennington, Richard Socher, Christopher D. Manning Conference paper 2014 34,197
2 SMOTE: Synthetic Minority Over-sampling Technique Nitesh V. Chawla, Kevin W. Bowyer, Lawrence Hall et al. · Journal of Artificial Intelligence Research Open access Jun 2002 32,759
3 Comparison of Convenience Sampling and Purposive Sampling İlker Etikan · American Journal of Theoretical and Applied Statistics Open access 2016 11,287
4 Semi-Supervised Classification with Graph Convolutional Networks Thomas Kipf, Max Welling · arXiv (Cornell University) PreprintOpen access 9 Sep 2016 8,050
5 Text categorization with Support Vector Machines: Learning with many relevant features Thorsten Joachims · Lecture notes in computer science Conference paper 1998 8,050
All 100 most cited papers, with filters and sorting

Where is Text and Document Classification Technologies research done?

The largest centres of Text and Document Classification Technologies research in 2022-now are Beijing (China), Nanjing (China), Shanghai (China) and Guangzhou (China). Among places with at least 20 works in it, it is an unusually large share of all research in Vijayawada.

Largest cities, 2022-now

  1. 1 BeijingChina 493 works
  2. 2 NanjingChina 155 works
  3. 3 ShanghaiChina 154 works
  4. 4 GuangzhouChina 144 works
  5. 5 Xi'anChina 137 works
  6. 6 ChengduChina 120 works
  7. 7 WuhanChina 111 works
  8. 8 ChennaiIndia 100 works
  9. 9 ChangshaChina 91 works
  10. 10 HangzhouChina 86 works

Where it is the local speciality

  1. VijayawadaIN · 21.5 works6.9×
← less than its size predicts1×more →

Location quotient: how much more of its research is in Text and Document Classification Technologies than the world average.

See Text and Document Classification Technologies on the map

Where is the best place to study Text and Document Classification Technologies?

Among universities, judged by research, National University of Defense Technology, Harbin Institute of Technology and Jilin 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.

0%20%40%mean 26.02%fractional works in this field (log) →share of work in the world top 10% →Jilin UniversityHarbin Institute of …National University …Southeast University
above the meannear itbelow it

One dot per university in the first ten of the table below, at the default weights. The upper left is the interesting corner: small places doing unusually strong work.

Excellenceshare of its work in the world top 10%, 2022-now and all time averaged
40%
Specialisationhow much of its work is in this field, counted up to 10× the world share
10%
Sizeits output in this field, on a log scale
30%
Growthoutput growth, counted from -20% to +20%
10%
Internationalshare of its work with a co-author abroad
10%
Each slider sets a weight; the share it gets of the score is beside it.
#UniversityScoreTop 10%SpecialisationWorksGrowth
1National University of Defense TechnologyChina 66.223.3%8.8×41 +43.7%
2Harbin Institute of TechnologyChina 64.026.0%3.7×36 +79.7%
3Jilin UniversityChina 63.933.9%3.2×22 +57.1%
4Southeast UniversityChina 62.817.0%3.8×34 +60.7%
5Nantong UniversityChina 61.145.2%3.7×10 +217.5%
6Hunan Normal UniversityChina 60.441.0%6.8×10 +260.9%
7Xidian UniversityChina 59.713.6%7.8×34 +198.1%
8Guangdong University of TechnologyChina 58.511.7%9.8×32 +168.2%
9Anhui UniversityChina 58.417.6%9.5×26 +95.3%
10Sun Yat-sen UniversityChina 57.830.9%1.7×21 +59.3%

Universities only. The score blends excellence (40%), size (30%), specialisation (10%), growth (10%), international reach (10%), 2022-now; growth compares 2018-21 with 2022-25 and counts up to +20%, since a jump from a small base is mostly noise.

Every institution in Text and Document Classification Technologies, country by country

Is Text and Document Classification Technologies research growing?

Output in 2022-2025 was 27% higher than in 2018-2021, peaking in 2025. The fastest-growing topics are Text and Document Classification Technologies.

still counting19801990200020102020
grewheldshrank

The same series as a ribbon: one cell per year, darker for more. The line above answers how much; this answers when.

Its share of all research

Growing output can still be a shrinking share: all of science grows. This is Text and Document Classification Technologies as a percentage of every work published that year, 0.029% in 2024.

still counting0.020%0.040%Text and Document Classification Technologies0.030% in 202519801990200020102020

Fractional works of Text and Document Classification Technologies over all fractional works that year. How the balance of science has shifted.

How much of it reaches the world top 10%

Year by year.

still counting5%10%15%Text and Document Classification Technologies9.9% in 2025200020102020

Of each year's works in it, the share that reached the world's 10% most cited for their field and year. A field is the world for its own topics, so there is no other line to hold it against.

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.

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