IMDb Movie Reviews with Sentiment Labels, 50,000 Samples

50,000 movie reviews from IMDb, each labeled as positive or negative for sentiment analysis. The dataset was sourced from the Kaggle platform, but the author, organization, and specific collection time range are not provided. Its primary purpose is for training and evaluating binary sentiment classification models.

Use Cases

• Train binary sentiment classifiers based on labeled review text.
• Benchmark model performance on a large-scale text classification task based on the 50,000 sample size.
• Analyze linguistic patterns distinguishing positive and negative movie critiques based on the sentiment labels.

Strengths

• Contains 50,000 labeled samples, providing a substantial corpus for model training.
• Focuses on a well-defined binary classification task (positive/negative sentiment).

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Last update date is unknown; freshness unverified.
• Data may reflect temporal or source bias inherent to the original IMDb collection.

Provenance

Source

IMDb, via Kaggle

Collection Method

Likely scraped or compiled from IMDb user reviews.