Mustafaege/imdb-sentiment

IMDb Sentiment Classification

A curated version of the Large Movie Review Dataset with custom train/validation/test splits optimized for model training and evaluation.

Dataset Summary

This dataset contains 50,000 labeled movie reviews from IMDb, each labeled as positive (1) or negative (0). The data originates from the Stanford AI Lab's Large Movie Review Dataset, re-split into 35k/5k/10k for better validation during training.

Splits

Split	Samples	Positive	Negative
train	35,000	17,500	17,500
validation	5,000	2,500	2,500
test	10,000	5,000	5,000
Total	50,000	25,000	25,000

The dataset is balanced — each split has roughly equal positive and negative reviews.

Data Fields

• text (string): The movie review text (English).
• label (int): Sentiment label — 0 for negative, 1 for positive.

Usage

from datasets import load_dataset

ds = load_dataset("Mustafaege/imdb-sentiment")

# Access splits
train_ds = ds["train"]       # 35,000 samples
val_ds = ds["validation"]    # 5,000 samples
test_ds = ds["test"]         # 10,000 samples

# Example
print(train_ds[0])
# {'text': 'This movie was absolutely fantastic...', 'label': 1}

Source

• Original dataset: Stanford Large Movie Review Dataset
• Original HF mirror: stanfordnlp/imdb
• Paper: Maas et al., "Learning Word Vectors for Sentiment Analysis", ACL 2011

Citation

@InProceedings{maas-EtAl:2011:ACL-HLT2011,
  author    = {Maas, Andrew L. and Daly, Raymond E. and Pham, Peter T. and Huang, Dan and Ng, Andrew Y. and Potts, Christopher},
  title     = {Learning Word Vectors for Sentiment Analysis},
  booktitle = {Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies},
  month     = {June},
  year      = {2011},
  address   = {Portland, Oregon, USA},
  publisher = {Association for Computational Linguistics},
  pages     = {142--150},
  url       = {http://www.aclweb.org/anthology/P11-1015}
}

License

The IMDb dataset is provided for academic research use. See the original dataset page for licensing details.