README.md

metadata

annotations_creators:
  - expert-generated
language_creators:
  - expert-generated
language:
  - en
license:
  - mit
multilinguality:
  - monolingual
pretty_name: sentiment-classification-reviews-with-drift
size_categories:
  - 10K<n<100K
source_datasets:
  - extended|imdb
task_categories:
  - text-classification
task_ids:
  - sentiment-classification

Dataset Card for reviews_with_drift

Table of Contents

• Table of Contents
• Dataset Description
  • Dataset Summary
  • Supported Tasks and Leaderboards
  • Languages
• Dataset Structure
  • Data Instances
  • Data Fields
  • Data Splits
• Dataset Creation
  • Curation Rationale
  • Source Data
  • Annotations
  • Personal and Sensitive Information
• Considerations for Using the Data
  • Social Impact of Dataset
  • Discussion of Biases
  • Other Known Limitations
• Additional Information
  • Dataset Curators
  • Licensing Information
  • Citation Information
  • Contributions

Dataset Description

Dataset Summary

This dataset was crafted to be used in our tutorial [Link to the tutorial when ready]. It consists on a large Movie Review Dataset mixed with some reviews from a Hotel Review Dataset. The training/validation set are purely obtained from the Movie Review Dataset while the production set is mixed. Some other features have been added (age, gender, context) as well as a made up timestamp prediction_ts of when the inference took place.

Supported Tasks and Leaderboards

text-classification, sentiment-classification: The dataset is mainly used for text classification: given the text, predict the sentiment (positive or negative).

Languages

Text is mainly written in english.

Dataset Structure

Data Instances

default

An example of training looks as follows:

{
 'prediction_ts': 1650092416.0,
 'age': 44,
 'gender': 'female',
 'context': 'movies',
 'text': "An interesting premise, and Billy Drago is always good as a dangerous nut-bag (side note: I'd love to see Drago, Stephen McHattie and Lance Hendrikson in a flick together; talk about raging cheekbones!). The soundtrack wasn't terrible, either.<br /><br />But the acting--even that of such professionals as Drago and Debbie Rochon--was terrible, the directing worse (perhaps contributory to the former), the dialog chimp-like, and the camera work, barely tolerable. Still, it was the SETS that got a big 10 on my oy-vey scale. I don't know where this was filmed, but were I to hazard a guess, it would be either an open-air museum, or one of those re-enactment villages, where everything is just a bit too well-kept to do more than suggest the real Old West. Okay, so it was shot on a college kid's budget. That said, I could have forgiven one or two of the aforementioned faults. But taken all together, and being generous, I could not see giving it more than three stars.",
 'label': 0
 }

Data Fields

default

The data fields are the same among all splits. An example of training looks as follows:

• prediction_ts: a float feature.
• age: an int feature.
• gender: a string feature.
• context: a string feature.
• text: a string feature.
• label: a ClassLabel feature, with possible values including negative(0) and positive(1).

Data Splits

name	training	validation	production
default	9916	2479	40079

Dataset Creation

Curation Rationale

[More Information Needed]

Contributions

Thanks to @fjcasti1 for adding this dataset.