|
|
--- |
|
|
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 |
|
|
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 |
|
|
|
|
|
--- |
|
|
<br> |
|
|
<br> |
|
|
<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> |
|
|
|
|
|
<br> |
|
|
<br> |
|
|
<br> |
|
|
|
|
|
<div style="text-align: center;"> |
|
|
<b></b><h1>1.5K Steam Reviews Binary Labeled for Constructiveness</h1></b> |
|
|
</div> |
|
|
<hr> |
|
|
|
|
|
## <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. |
|
|
|
|
|
The dataset is particularly useful for training models like **BERT**, and its' derivatives or any other NLP models aimed at classifying text. |
|
|
|
|
|
## <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. |
