This project is done as a part of Metis Kaplan course requirement in collaboration with Fahad Alnafisa
The game industry is projected to grow to 9.64% by 2026 (mordointelligence.com). The exponential growth rate leads to better understanding of gamers need in order to compete in this industry. While game studios try their best to achieve players expectations, some of the gamers do not appreciate efforts put in developing games. Negative reviews are most likely associated with profanity. In our project, we seek to identify profranity based on negative portion of reviews. In addition, to build a recommendation system using collaborate filtering engine to match gamers with same similar taste in games.
In our project, we are trying to identify whether the review made by player contains profanity. Moreover, build a model that predicts whether a certain review is considered profanity one.
This data has been obtained from Kaggle originally fetched using SteamAPI. The data is around 21 million records with 23 features (21 million reviews across more than 300 games). The features lists as follow:
| Feature | Description |
|---|---|
| app_id | AppID of the game |
| app_name | name of the game |
| review_id | intuitive |
| language | language the user indicated when authoring the review |
| review | text-based review |
| timestamp_created | date the review was created (unix timestamp) |
| timestamp_updated | date the review was last updated (unix timestamp) |
| recommended | whether review recommends the app |
| votes_helpful | the number of users that found this review helpful |
| votes_funny | the number of users that found this review funny |
| weighted_vote_score | helpfulness score |
| comment_count | number of comments posted on this review |
| steam_purchase | true if the user purchased the game on Steam |
| received_for_free | true if the user checked a box saying they got the app for free |
| written_during_early_access | true if the user posted this review while the game was in Early Access |
| author.steamid | the user’s SteamID |
| author.num_games_owned | number of games owned by the user |
| author.num_reviews | number of reviews written by the user |
| author.playtime_forever | lifetime playtime tracked in this app |
| author.playtime_last_two_weeks | playtime tracked in the past two weeks for this app |
| author.playtime_at_review | playtime when the review was written |
| author.last_played | time for when the user last played |
- Python
- Pandas
- Numpy
- Scikit-learn
- Matplotlib
- Seaborn