metadata

license: mit
task_categories:
  - text-classification
language:
  - en
multilinguality:
  - monolingual
annotations_creators:
  - expert-generated
language_creators:
  - found
source_datasets:
  - extended
dataset_modality: text
tags:
  - gaming
  - annotations
  - binary
  - classification
  - labels
  - steam
  - reviews
  - steam-reviews
  - BERT
  - ROBERTA
  - constructiveness
  - constructivity
  - sentiment-analysis
  - nlp
pretty_name: 1.5K Steam Reviews Binary Labeled for Constructiveness
size_categories:
  - 1K<n<10K
thumbnail: >-
  https://i.ibb.co/Bnj0gw6/abullard1-steam-review-constructiveness-classifier-logo.png
dataset_size: 332 KB
dataset_info:
  features:
    - name: id
      dtype: int32
    - name: game
      dtype: string
    - name: review
      dtype: string
    - name: author_playtime_at_review
      dtype: int32
    - name: voted_up
      dtype: bool
    - name: votes_up
      dtype: int32
    - name: votes_funny
      dtype: int32
    - name: constructive
      dtype: int32

1.5K Steam Reviews Binary Labeled for Constructiveness

Dataset Summary

This dataset contains 1,461 Steam reviews from 10 of the most reviewed games. Each game has about the same amount of reviews. Each review is annotated with a binary label indicating whether the review is constructive or not. The dataset is designed to support tasks related to text classification, particularly constructiveness detection tasks in the gaming domain.

The dataset is particularly useful for training models like BERT, and its' derivatives or any other NLP models aimed at classifying text.

Dataset Structure

The dataset contains the following columns:

id: A unique identifier for each review.
game: The name of the game being reviewed.
review: The text of the Steam review.
author_playtime_at_review: The number of hours the author had played the game at the time of writing the review.
voted_up: Whether the user marked the review/the game as positive (True) or negative (False).
votes_up: The number of upvotes the review received from other users.
votes_funny: The number of "funny" votes the review received from other users.
constructive: A binary label indicating whether the review was constructive (1) or not (0).

Example Data

id	game	review	author_playtime_at_review	voted_up	votes_up	constructive
1024	Team Fortress 2	shoot enemy	639	True	1	0
652	Grand Theft Auto V	6 damn years and it's still rocking like its g...	145	True	0	0
1244	Terraria	Great game highly recommend for people who like...	569	True	0	1
15	Among Us	So good. Amazing game of teamwork and betrayal...	5	True	0	1
584	Garry's Mod	Jbmod is trash!!!	65	True	0	0

Labeling Criteria

Constructive (1): Reviews that provide helpful feedback, suggestions for improvement, constructive criticism, or detailed insights into the game.
Non-constructive (0): Reviews that do not offer useful feedback, do not offer substance, are vague, off-topic, irrelevant, or trolling.

Of course, constructiveness is subjective, when testing the dataset on a finetuned ROBERTA model though, we reached about 80% accuracy.

Notes

Please note, that the dataset is unbalanced. 63.04% of the reviews were labeled as being non-constructive while 36.96% were labeled as being constructive. Please take this into account when utilizing the dataset.

License

This dataset is licensed under the MIT License, allowing open and flexible use of the dataset for both academic and commercial purposes.