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.
NLP dataset comparison: tasks, access and usage conditions
Dataset / version
Task and evaluation
Access and preparation
Usage conditions
IMDb (Large Movie Review Dataset)
Binary sentiment classification of long movie reviews.
Evaluation: Accuracy; keep the official split and distinguish document-level reviews from sentence sentiment.
From Nov 2023 to Oct 2026, 202 archived papers mentioned IMDb in their title, abstract or keywords (the search of earlier months' full texts is still under way), most of them in Jan 2025 (12).
IMDb was mentioned in 1 paper in the last 30 days (2026-10-06 to 2026-11-04), ranking #172 of the 255 datasets mentioned.
By journal category: AI (1).
Most of them appeared in Expert Systems with Applications (1).
From 2017 to 2026 (so far), IMDb was mentioned in 235 papers in the tracked journals, most of them in AI (192), information systems (12) and marketing (9).
68 of them were published in 2025, against 6 in 2017.
Since 2023, the institutions with the most of these papers were Hong Kong Polytechnic University (5), Sun Yat-sen University (5) and Tsinghua University (5).
The journals publishing most of them were Artificial Intelligence Review (24), IEEE Transactions on Knowledge and Data Engineering (16) and IEEE Transactions on Neural Networks and Learning Systems (15).
The most-cited of them is “Autoencoders and their applications in machine learning: a survey” (Artificial Intelligence Review, 2024), with 613 citations.
Papers use IMDb most often with Twitter / X (51), Yelp (38) and CIFAR-10 / CIFAR-100 (37); it has 47 partners in the code and data network, in the group around Twitter / X, Facebook, YouTube / TikTok.
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)
Achhardeep Kaur, Azadeh Noori Hoshyar, Vidya Saikrishna et al.
Archive counts by publication year: a paper counts when its title, abstract or keywords name the dataset, or when OpenAlex's full-text search finds it there (searched for the last 30 days' papers and, by a pass over the past years, for older ones). Names added to the list later are looked for in older papers as the archive is read again.
Papers use IMDb most often with Twitter / X (51), Yelp (38) and CIFAR-10 / CIFAR-100 (37); it has 47 partners in the code and data network, in the group around Twitter / X, Facebook, YouTube / TikTok. The whole network →
What papers use IMDb together with (at least 3 papers; lift 1 = chance)
Scholar4AI. (2026). IMDb. Journal and Conference Research Rankings. Retrieved November 4, 2026, from https://scholar4ai.com/datasets/imdb
@misc{scholar4ai-datasets-imdb,
author = {{Scholar4AI}},
title = {IMDb},
year = {2026},
howpublished = {\url{https://scholar4ai.com/datasets/imdb}},
urldate = {2026-11-04},
note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}
Free to reuse for non-commercial purposes with attribution (CC BY-NC 4.0).