Datasets:
Tasks:
Text Classification
Formats:
csv
Sub-tasks:
sentiment-classification
Languages:
English
Size:
10K - 100K
License:
| 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](#table-of-contents) | |
| - [Dataset Description](#dataset-description) | |
| - [Dataset Summary](#dataset-summary) | |
| - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) | |
| - [Languages](#languages) | |
| - [Dataset Structure](#dataset-structure) | |
| - [Data Instances](#data-instances) | |
| - [Data Fields](#data-fields) | |
| - [Data Splits](#data-splits) | |
| - [Dataset Creation](#dataset-creation) | |
| - [Curation Rationale](#curation-rationale) | |
| - [Source Data](#source-data) | |
| - [Annotations](#annotations) | |
| - [Personal and Sensitive Information](#personal-and-sensitive-information) | |
| - [Considerations for Using the Data](#considerations-for-using-the-data) | |
| - [Social Impact of Dataset](#social-impact-of-dataset) | |
| - [Discussion of Biases](#discussion-of-biases) | |
| - [Other Known Limitations](#other-known-limitations) | |
| - [Additional Information](#additional-information) | |
| - [Dataset Curators](#dataset-curators) | |
| - [Licensing Information](#licensing-information) | |
| - [Citation Information](#citation-information) | |
| - [Contributions](#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: | |
| ```json | |
| { | |
| '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](https://github.com/fjcasti1) for adding this dataset. |