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--- |
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license: mit |
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task_categories: |
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- text-classification |
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language: |
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- en |
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multilinguality: |
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- monolingual |
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annotations_creators: |
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- expert-generated |
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language_creators: |
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- found |
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source_datasets: |
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- extended |
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dataset_modality: text |
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tags: |
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- gaming |
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- annotations |
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- binary |
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- classification |
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- labels |
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- steam |
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- reviews |
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- steam-reviews |
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- BERT |
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- ROBERTA |
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- constructiveness |
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- constructivity |
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- sentiment-analysis |
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- nlp |
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pretty_name: 1.5K Steam Reviews Binary Labeled for Constructiveness |
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size_categories: |
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- 1K<n<10K |
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thumbnail: https://i.ibb.co/Bnj0gw6/abullard1-steam-review-constructiveness-classifier-logo.png |
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dataset_size: 332 KB |
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dataset_info: |
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features: |
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- name: id |
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dtype: int32 |
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- name: game |
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dtype: string |
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- name: review |
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dtype: string |
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- name: author_playtime_at_review |
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dtype: int32 |
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- name: voted_up |
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dtype: bool |
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- name: votes_up |
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dtype: int32 |
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- name: votes_funny |
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dtype: int32 |
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- name: constructive |
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dtype: int32 |
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--- |
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# 1.5K Steam Reviews Binary Labeled for Constructiveness |
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## Dataset Summary |
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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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The dataset is particularly useful for training models like **BERT**, and its' derivatives or any other NLP models aimed at classifying text. |
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## Dataset Structure |
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The dataset contains the following columns: |
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- **id**: A unique identifier for each review. |
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- **game**: The name of the game being reviewed. |
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- **review**: The text of the Steam review. |
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- **author_playtime_at_review**: The number of hours the author had played the game at the time of writing the review. |
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- **voted_up**: Whether the user marked the review/the game as positive (True) or negative (False). |
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- **votes_up**: The number of upvotes the review received from other users. |
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- **votes_funny**: The number of "funny" votes the review received from other users. |
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- **constructive**: A binary label indicating whether the review was constructive (1) or not (0). |
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### Example Data |
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| id | game | review | author_playtime_at_review | voted_up | votes_up | votes_funny | constructive | |
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|------|---------------------|-------------------------------------------------------------------------|---------------------------|----------|----------|-------------|--------------| |
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| 1024 | Team Fortress 2 | shoot enemy | 639 | True | 1 | 0 | 0 | |
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| 652 | Grand Theft Auto V | 6 damn years and it's still rocking like its g... | 145 | True | 0 | 0 | 0 | |
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| 1244 | Terraria | Great game highly recommend for people who like... | 569 | True | 0 | 0 | 1 | |
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| 15 | Among Us | So good. Amazing game of teamwork and betrayal... | 5 | True | 0 | 0 | 1 | |
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| 584 | Garry's Mod | Jbmod is trash!!! | 65 | True | 0 | 0 | 0 | |
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### Labeling Criteria |
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- **Constructive (1)**: Reviews that provide helpful feedback, suggestions for improvement, constructive criticism, or detailed insights into the game. |
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- **Non-constructive (0)**: Reviews that do not offer useful feedback, do not offer substance, are vague, off-topic, irrelevant, or trolling. |
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Of course, constructiveness is subjective, when testing the dataset on a finetuned ROBERTA model though, we reached about 80% accuracy. |
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### Notes |
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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. |
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## License |
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This dataset is licensed under the **MIT License**, allowing open and flexible use of the dataset for both academic and commercial purposes. |
