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Sentiment Analysis and Opinion Mining

Sentiment Analysis and Opinion Mining is a research topic within Artificial Intelligence. Science Explorer counts 26k research works in it since 1970. 25.5% of them reached the world's top 10% most cited for their field and year.

This cluster of papers focuses on sentiment analysis and opinion mining, particularly in the context of social media and text mining. It covers various techniques such as lexicon-based methods, deep learning, aspect-based sentiment analysis, and machine learning for analyzing emotions and opinions in textual data from platforms like Twitter. The research also delves into emotion recognition and the impact of sentiment analysis on public perception.

  • Sentiment Analysis
  • Opinion Mining
  • Social Media
  • Text Mining
  • Emotion Recognition
  • Deep Learning
  • Aspect-based Sentiment Analysis
  • Lexicon-Based Methods
  • Twitter Sentiment
  • Machine Learning
Research works
26k
fractional, since 1970
In the world top 10%
6.6k
per year above
Top-10% rate
25.5%
share of its works in the world top 10%
Growth, 2013–17 → 2018–22
+160%
the tick is no change

Which countries lead Sentiment Analysis and Opinion Mining research?

By volume, China and India publish the most (2.8k and 2.6k works in 2022–2025).

By volume, 2022–2025

  1. 1 China 2.8k works
  2. 2 India 2.6k works
  3. 3 Indonesia 895 works
  4. 4 United States 857 works
  5. 5 United Kingdom 235 works
  6. 6 Türkiye 234 works
  7. 7 Bangladesh 229 works
  8. 8 Malaysia 228 works
  9. 9 Saudi Arabia 202 works
  10. 10 ?? 192 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: 32.6%India: 31.1%Indonesia: 10.6%United States: 10.1%6 others listed: 15.6%33%largest
China2,761 · 32.6%India2,635 · 31.1%Indonesia895 · 10.6%United States857 · 10.1%6 others listed1,320 · 15.6%

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

Which institutions lead Sentiment Analysis and Opinion Mining research?

By volume in 2022–2025, Telkom University publishes the most Sentiment Analysis and Opinion Mining research, followed by Binus University and SRM Institute of Science and Technology.

Who are the leading researchers in Sentiment Analysis and Opinion Mining?

The most-cited researchers publishing on Sentiment Analysis and Opinion Mining include Philip S. Yu, Christopher D. Manning and Jure Leskovec.

  1. 1 Philip S. Yu United States 6.6k citations
  2. 2 Christopher D. Manning United States 4.6k citations
  3. 3 Jure Leskovec United States 4.3k citations

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

Where is Sentiment Analysis and Opinion Mining research done?

The largest centres of Sentiment Analysis and Opinion Mining research in 2022–2025 are Beijing (China), Chennai (India), Jakarta (Indonesia) and Shanghai (China). Among places with at least 20 works in it, it is an unusually large share of all research in Chittagong, Faridabad and Vijayawada.

Largest cities, 2022–2025

  1. 1 Beijing China 466 works
  2. 2 Chennai India 259 works
  3. 3 Jakarta Indonesia 192 works
  4. 4 Shanghai China 177 works
  5. 5 Bandung Indonesia 173 works
  6. 6 Bengaluru India 150 works
  7. 7 Guangzhou China 149 works
  8. 8 Dhaka Bangladesh 145 works
  9. 9 New Delhi India 143 works
  10. 10 Wuhan China 139 works

Where it is the local speciality

  1. ChittagongBD · 44.8 works11×
  2. FaridabadIN · 25.5 works11×
  3. VijayawadaIN · 55.1 works11×
  4. Greater NoidaIN · 66.9 works9.5×
← less than its size predictsmore →

Location quotient: how much more of its research is in Sentiment Analysis and Opinion Mining than the world average.

See Sentiment Analysis and Opinion Mining on the map

Where is the best place to study Sentiment Analysis and Opinion Mining?

Among universities, judged by research, Indian Institute of Technology Patna, Amrita Vishwa Vidyapeetham and Nanyang Technological 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%60%mean 31.53%fractional works in this node (log) →share in the world top 10% →Indian Institute of Technology Patna: 22, 38.5%Amrita Vishwa Vidyapeetham: 67, 21.0%Nanyang Technological University: 28, 49.9%Daffodil International University: 26, 28.6%Communication University of China: 18, 25.1%Singapore Management University: 9, 44.3%Delhi Technological University: 48, 27.2%Multimedia University: 16, 32.7%King Saud University: 24, 37.7%Telkom University: 116, 10.3%Nanyang Technologica…Indian Institute of …Daffodil Internation…Amrita Vishwa Vidyap…
above the meannear itbelow it

One dot per university in the table below. The upper left is the interesting corner: small places doing unusually strong work.

#UniversityScoreTop 10%SpecialisationWorksGrowth
1 Indian Institute of Technology PatnaIndia 71.638.5%14.5×22 +552.3%
2 Amrita Vishwa VidyapeethamIndia 69.821.0%11.2×67 +1335.4%
3 Nanyang Technological UniversitySingapore 63.349.9%3.1×28 +117.1%
4 Daffodil International UniversityBangladesh 62.628.6%24.6×26
5 Communication University of ChinaChina 62.425.1%15.6×18 +201.2%
6 Singapore Management UniversitySingapore 61.344.3%7.6×9 +96.0%
7 Delhi Technological UniversityIndia 61.127.2%15.6×48
8 Multimedia UniversityMalaysia 58.932.7%10.5×16
9 King Saud UniversitySaudi Arabia 58.137.7%2.4×24 +387.5%
10 Telkom UniversityIndonesia 57.710.3%19.6×116

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 Sentiment Analysis and Opinion Mining research growing?

Output in 2018–2022 was 160% higher than in 2013–2017, peaking in 2025. The fastest-growing topics are Sentiment Analysis and Opinion Mining.

19801990200020102020
grewheldshrank

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.