README.md

---
license: mit
task_categories:
- text-classification
license_link: https://mit-license.org/
language:
- en
multilinguality:
- monolingual
annotations_creators:
- expert-generated
language_creators:
- found
source_datasets:
- extended
dataset_modality: text
viewer: true
configs:
- config_name: main_data
  data_files:
    - split: base
      path: "steam_reviews_constructiveness_1.5k.csv"
- config_name: additional_data
  data_files:
    - split: train
      path: "train-dev-test_split_csvs/train.csv"
    - split: dev
      path: "train-dev-test_split_csvs/dev.csv"
    - split: test
      path: "train-dev-test_split_csvs/test.csv"
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/Ky0wcYy/abullard1-steam-review-constructiveness-classifier-logo-modified-1.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

---
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<div style="text-align: center;">
    <img src="https://i.ibb.co/Ky0wcYy/abullard1-steam-review-constructiveness-classifier-logo-modified-1.png" style="max-width: 30%; display: block; margin: 0 auto;">
</div>

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<div style="text-align: center;">
    <b></b><h1>1.5K Steam Reviews Binary Labeled for Constructiveness</h1></b>
</div>
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## <u>Dataset Summary</u>

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.
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Also available as additional data, are **train/dev/test split** csv's. These contain the features of the base dataset, concatenated into strings, next to the binary constructiveness labels. These csv's were used to train the [albert-v2-steam-review-constructiveness-classifier](https://huggingface.co/abullard1/albert-v2-steam-review-constructiveness-classifier) model.
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The dataset is particularly useful for training models like **BERT**, and its' derivatives or any other NLP models aimed at classifying text for constructiveness.

## <u>Dataset Structure</u>

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 | votes_funny | constructive |
|------|---------------------|-------------------------------------------------------------------------|---------------------------|----------|----------|-------------|--------------|
| 1024 | Team Fortress 2      | shoot enemy                                                            | 639                       | True     | 1        | 0           | 0            |
| 652  | Grand Theft Auto V   | 6 damn years and it's still rocking like its g...                       | 145                       | True     | 0        | 0           | 0            |
| 1244 | Terraria             | Great game highly recommend for people who like...                      | 569                       | True     | 0        | 0           | 1            |
| 15   | Among Us             | So good. Amazing game of teamwork and betrayal...                       | 5                         | True     | 0        | 0           | 1            |
| 584  | Garry's Mod          | Jbmod is trash!!!                                                       | 65                        | True     | 0        | 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 managed to reach **~80% accuracy** in predicting constructiveness labels on unseen data.

### 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.

## <u>License</u>

This dataset is licensed under the *[MIT License](https://mit-license.org/)*, allowing open and flexible use of the dataset for both academic and commercial purposes.