---
title: "Stanford Sentiment Treebank: tasks and access"
description: "Stanford Sentiment Treebank: compare tasks, official access, usage conditions and evaluation. Includes research-paper mention counts and trends."
url: https://scholar4ai.com/datasets/stanford-sentiment-treebank
markdown_url: https://scholar4ai.com/datasets/stanford-sentiment-treebank.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "Stanford Sentiment Treebank: compare tasks, official access, usage conditions and evaluation. Includes research-paper mention counts and trends. (as of 2026-11-05)"
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-11-05T06:02:37+09:00
window: 2026-10-07 to 2026-11-05
source: OpenAlex (CC0)
---

# Stanford Sentiment Treebank

[Browse dataset profiles](https://scholar4ai.com/datasets/directory/page/6.md)

Corpora and benchmarks for language understanding, question answering and text classification.

- data category: Text and NLP benchmarks
- 0 papers in the last 7 days
- Names searched for: Stanford Sentiment Treebank, SST-2, SST-5

Most-used data: https://scholar4ai.com/rankings/datasets.md · All Text and NLP benchmarks datasets: https://scholar4ai.com/datasets/text-and-nlp-benchmarks.md

## Tasks, access and usage conditions

Mention counts below measure name matches in tracked papers, not benchmark quality or downloads. A family count can cover multiple datasets; it is not a separate count for each comparison row. Choose a task and version first, preserve evaluation splits, and follow the linked release terms. The site's content license does not replace the datasets' own terms.

*Dataset comparison: tasks, access and usage conditions*

| Dataset / version | Task and evaluation | Access and preparation | Usage conditions |
|---|---|---|---|
| Stanford Sentiment Treebank (SST) | Sentence and phrase sentiment; SST-2 is binary, SST-5 uses five labels. Evaluation: Accuracy; sentence-only and phrase-augmented experiments are not directly equivalent. | [Official source / documentation](https://nlp.stanford.edu/sentiment/) Download the Stanford treebank; choose binary or five-class setup and state whether training uses phrase labels. | Check the original release terms and underlying movie-review rights. GLUE packaging does not grant a new blanket license. |

Guide updated 2026-11-05.

## Key findings

- From Nov 2023 to Oct 2026, 53 archived papers mentioned Stanford Sentiment Treebank in their title, abstract or keywords (the search of earlier months' full texts is still under way), most of them in Dec 2024 (6).
- No paper of the tracked journals mentioned Stanford Sentiment Treebank in the last 30 days (2026-10-07 to 2026-11-05).
- From 2017 to 2026 (so far), Stanford Sentiment Treebank was mentioned in 58 papers in the tracked journals, most of them in AI (54), information systems (1) and finance (1).
- 10 of them were published in 2025, against 0 in 2017.
- Since 2023, the institutions with the most of these papers were Nanyang Technological University (5), Chinese Academy of Sciences (4) and The University of Sydney (4).
- The journals publishing most of them were IEEE Transactions on Neural Networks and Learning Systems (7), Artificial Intelligence Review (5) and Neural Networks (5).
- The most-cited of them is “Vision-Language Models for Vision Tasks: A Survey” (IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024), with 998 citations.
- Papers use Stanford Sentiment Treebank most often with Natural language inference (SNLI, MultiNLI) (27), IMDb (21) and GLUE / SuperGLUE (19); it has 33 partners in the code and data network, in the group around Twitter / X, Facebook, YouTube / TikTok.

## Mentions over 36 months

*Papers mentioning it by publication month, Nov 2023 to Oct 2026 (titles, abstracts and keywords; the full texts of earlier months are still being searched)*

| Trend | Nov 23–Oct 24 | Nov 24–Oct 25 | Nov 25–Oct 26 | 36 months |
|---|---:|---:|---:|---:|
|  | 20 | 15 | 18 | 53 |

## Papers per year

*Papers per publication year and category*

| Category | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 | 2023 | 2024 | 2025 | 2026 (so far) |
|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|
| [AI](https://scholar4ai.com/categories/ai.md) | 0 | 0 | 0 | 1 | 1 | 0 | 5 | 22 | 10 | 15 |
| [Information Systems](https://scholar4ai.com/categories/information-systems.md) | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| [Finance](https://scholar4ai.com/categories/finance.md) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| [Operations Research](https://scholar4ai.com/categories/operations-research.md) | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| [Economics](https://scholar4ai.com/categories/economics.md) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| All categories | 0 | 0 | 0 | 1 | 1 | 1 | 6 | 23 | 10 | 16 |

## Where the papers come from

*Journals with the most papers, since 2023*

| # | Journal | Papers |
|---:|---|---:|
| 1 | [IEEE Transactions on Neural Networks and Learning Systems](https://scholar4ai.com/journals/ieee-transactions-on-neural-networks-and-learning-systems.md) | 7 |
| 2 | [Artificial Intelligence Review](https://scholar4ai.com/journals/artificial-intelligence-review.md) | 5 |
| 3 | [Neural Networks](https://scholar4ai.com/journals/neural-networks.md) | 5 |
| 4 | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | 5 |
| 5 | [IEEE Transactions on Pattern Analysis and Machine Intelligence](https://scholar4ai.com/journals/ieee-transactions-on-pattern-analysis-and-machine-intelligence.md) | 4 |
| 6 | [Information Fusion](https://scholar4ai.com/journals/information-fusion.md) | 4 |
| 7 | [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | 3 |
| 8 | [Journal Of Big Data](https://scholar4ai.com/journals/journal-of-big-data.md) | 3 |
| 9 | [Information Processing & Management](https://scholar4ai.com/journals/information-processing-management.md) | 2 |
| 10 | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | 2 |

*Institutions with the most papers, since 2023*

| # | Institution | Papers |
|---:|---|---:|
| 1 | [Nanyang Technological University](https://scholar4ai.com/institutions/nanyang-technological-university.md) | 5 |
| 2 | [Chinese Academy of Sciences](https://scholar4ai.com/institutions/chinese-academy-of-sciences.md) | 4 |
| 3 | [The University of Sydney](https://scholar4ai.com/institutions/the-university-of-sydney.md) | 4 |
| 4 | [Chinese University of Hong Kong](https://scholar4ai.com/institutions/chinese-university-of-hong-kong.md) | 3 |
| 5 | [Wuhan University](https://scholar4ai.com/institutions/wuhan-university.md) | 3 |
| 6 | [Alibaba Group (China)](https://scholar4ai.com/companies/alibaba-group-china.md) | 2 |
| 7 | [Anhui University](https://scholar4ai.com/institutions/anhui-university.md) | 2 |
| 8 | [Beihang University](https://scholar4ai.com/institutions/beihang-university.md) | 2 |
| 9 | [Harbin Institute of Technology](https://scholar4ai.com/institutions/harbin-institute-of-technology.md) | 2 |
| 10 | [Hong Kong Polytechnic University](https://scholar4ai.com/institutions/hong-kong-polytechnic-university.md) | 2 |

*Countries with the most papers, since 2023*

| # | Country | Papers |
|---:|---|---:|
| 1 | China | 28 |
| 2 | Australia | 6 |
| 3 | Hong Kong | 6 |
| 4 | Singapore | 6 |
| 5 | United Kingdom | 4 |
| 6 | United States | 4 |
| 7 | Germany | 3 |
| 8 | South Korea | 3 |
| 9 | Egypt | 2 |
| 10 | France | 2 |

*Keywords with the most papers, since 2023*

| # | Keyword | Papers |
|---:|---|---:|
| 1 | [large language models](https://scholar4ai.com/keywords/large-language-models.md) | 14 |
| 2 | [BERT](https://scholar4ai.com/keywords/bert.md) | 8 |
| 3 | text classification | 5 |
| 4 | [explainable AI](https://scholar4ai.com/keywords/explainable-ai.md) | 4 |
| 5 | language models | 4 |
| 6 | low-rank adaptation | 4 |
| 7 | model compression | 4 |
| 8 | parameter-efficient fine-tuning | 4 |
| 9 | pre-trained language models | 4 |
| 10 | [sentiment analysis](https://scholar4ai.com/keywords/sentiment-analysis.md) | 4 |

## Most-cited papers

*Most-cited papers since 2017*

| # | Citations | Per month | Title | Journal | Published | Authors |
|---:|---:|---:|---|---|---|---|
| 1 | 998 | 30.9 | [Vision-Language Models for Vision Tasks: A Survey](https://doi.org/10.1109/tpami.2024.3369699) | [IEEE Transactions on Pattern Analysis and Machine Intelligence](https://scholar4ai.com/journals/ieee-transactions-on-pattern-analysis-and-machine-intelligence.md) | 2024-02-26 | Jingyi Zhang, Jiaxing Huang, Sheng Jin et al. |
| 2 | 512 | 19.3 | [A Survey on Deep Neural Network Pruning: Taxonomy, Comparison, Analysis, and Recommendations](https://doi.org/10.1109/tpami.2024.3447085) | [IEEE Transactions on Pattern Analysis and Machine Intelligence](https://scholar4ai.com/journals/ieee-transactions-on-pattern-analysis-and-machine-intelligence.md) | 2024-08-21 | Hongrong Cheng, Miao Zhang, Qinfeng Shi |
| 3 | 271 | 10.4 | [A comprehensive review of model compression techniques in machine learning](https://doi.org/10.1007/s10489-024-05747-w) | [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | 2024-09-02 | Pierre V. Dantas, Waldir Sabino da Silva, Lucas Carvalho Cordeiro et al. |
| 4 | 133 | 4.5 | [A Survey on Deep Active Learning: Recent Advances and New Frontiers](https://doi.org/10.1109/tnnls.2024.3396463) | [IEEE Transactions on Neural Networks and Learning Systems](https://scholar4ai.com/journals/ieee-transactions-on-neural-networks-and-learning-systems.md) | 2024-05-23 | Dongyuan Li, Zhen Rong Wang, Yankai Chen et al. |
| 5 | 88 | 3.1 | [Graph neural networks for text classification: a survey](https://doi.org/10.1007/s10462-024-10808-0) | [Artificial Intelligence Review](https://scholar4ai.com/journals/artificial-intelligence-review.md) | 2024-07-01 | Kunze Wang, Yihao Ding, Soyeon Caren Han |

## Used together with

Papers use Stanford Sentiment Treebank most often with Natural language inference (SNLI, MultiNLI) (27), IMDb (21) and GLUE / SuperGLUE (19); it has 33 partners in the code and data network, in the group around Twitter / X, Facebook, YouTube / TikTok.

*What papers use Stanford Sentiment Treebank together with (at least 3 papers; lift 1 = chance)*

| Used with | Type | Papers using both | Lift |
|---|---|---:|---:|
| [Natural language inference (SNLI, MultiNLI)](https://scholar4ai.com/datasets/natural-language-inference-snli-multinli.md) | Dataset | 27 | 236.9 |
| [IMDb](https://scholar4ai.com/datasets/imdb.md) | Dataset | 21 | 73.7 |
| [GLUE / SuperGLUE](https://scholar4ai.com/datasets/glue-superglue.md) | Dataset | 19 | 145.1 |
| [Twitter / X](https://scholar4ai.com/datasets/twitter-x.md) | Dataset | 18 | 3.4 |
| [Hugging Face Hub](https://scholar4ai.com/datasets/hugging-face-hub.md) | Dataset | 15 | 26.5 |
| [Wikipedia](https://scholar4ai.com/datasets/wikipedia.md) | Dataset | 15 | 12.2 |
| [AG News / 20 Newsgroups](https://scholar4ai.com/datasets/ag-news-20-newsgroups.md) | Dataset | 13 | 54.7 |
| [CIFAR-10 / CIFAR-100](https://scholar4ai.com/datasets/cifar-10-cifar-100.md) | Dataset | 12 | 7.2 |
| [ImageNet](https://scholar4ai.com/datasets/imagenet.md) | Dataset | 12 | 4.2 |
| [Yelp](https://scholar4ai.com/datasets/yelp.md) | Dataset | 8 | 18.1 |
| [MNIST / Fashion-MNIST](https://scholar4ai.com/datasets/mnist-fashion-mnist.md) | Dataset | 7 | 5.2 |
| [MS COCO](https://scholar4ai.com/datasets/ms-coco.md) | Dataset | 6 | 5.3 |

The whole network: https://scholar4ai.com/code/network.md

## Cite this page

Scholar4AI. (2026). Stanford Sentiment Treebank. Journal and Conference Research Rankings. Retrieved November 5, 2026, from https://scholar4ai.com/datasets/stanford-sentiment-treebank

```bibtex
@misc{scholar4ai-datasets-stanford-sentiment-treebank,
  author = {{Scholar4AI}},
  title = {Stanford Sentiment Treebank},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/datasets/stanford-sentiment-treebank}},
  urldate = {2026-11-05},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}
```

License: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).