The Large Movie Review Dataset
Content and format of the raw dataset
The raw dataset contains movie reviews along with their associated binary category: positive or negative. The dataset is intended to serve as a benchmark for sentiment classification.
The core dataset contains 50,000 reviews split evenly into a training and test subset. The overall distribution of labels is balanced, i.e., there are 25,000 positive and 25,000 negative reviews.
The raw dataset also includes 50,000 unlabeled reviews for unsupervised learning, these will not be used in this tutorial.
In the entire collection, no more than 30 reviews are allowed for any given movie because reviews for the same movie tend to have correlated ratings.
In the labeled train/test sets, a negative review has a score that is less or equal to 4 out of 10, and a positive review has a score that is higher than 7. Reviews with more neutral ratings are not included in the dataset.
Each review is stored in a separate text file, located in a folder named either “positive” or “negative.”
Note: For more information about the raw dataset, see the ACL 2011 paper "Learning Word Vectors for Sentiment Analysis".
Written by Maas, A., Daly, R., Pham, P., Huang, D., Ng, A. and Potts, C. (2011). Learning Word Vectors for Sentiment Analysis: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies. [online] Portland, Oregon, USA: Association for Computational Linguistics, pp.142–150. Available at: http://www.aclweb.org/anthology/P11-1015.
Data library dataset is preprocessed
The dataset available in the data library tutorial has been preprocessed so that all the reviews and their respective sentiments are stored in a single CSV file with two fields, “review” and “sentiment.”
The review text may include commas, which will be interpreted as a field delimiter on the platform. To escape these commas, the text is surrounded by double-quotes.
The processed dataset only includes the training data.
Explore the dataset with Python
If you are familiar with Python and want to learn how the raw data was processed, you may want to try to generate it yourself using this Jupyter notebook. Among other things, it will give you some insight into why certain values are used later on in the tutorial.
To run the notebook you must either save or clone the entire GitHub repository or save the file in raw format with the .ipynb extension.