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| Main Author: | |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.06306 |
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| _version_ | 1866916241704222720 |
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| author | Coronado-Blázquez, Javier |
| author_facet | Coronado-Blázquez, Javier |
| contents | Many videogames suffer "review bombing" -a large volume of unusually low scores that in many cases do not reflect the real quality of the product- when rated by users. By taking Metacritic's 50,000+ user score aggregations for PC games in English language, we use a Natural Language Processing (NLP) approach to try to understand the main words and concepts appearing in such cases, reaching a 0.88 accuracy on a validation set when distinguishing between just bad ratings and review bombings. By uncovering and analyzing the patterns driving this phenomenon, these results could be used to further mitigate these situations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_06306 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A NLP Approach to "Review Bombing" in Metacritic PC Videogames User Ratings Coronado-Blázquez, Javier Computation and Language Machine Learning Many videogames suffer "review bombing" -a large volume of unusually low scores that in many cases do not reflect the real quality of the product- when rated by users. By taking Metacritic's 50,000+ user score aggregations for PC games in English language, we use a Natural Language Processing (NLP) approach to try to understand the main words and concepts appearing in such cases, reaching a 0.88 accuracy on a validation set when distinguishing between just bad ratings and review bombings. By uncovering and analyzing the patterns driving this phenomenon, these results could be used to further mitigate these situations. |
| title | A NLP Approach to "Review Bombing" in Metacritic PC Videogames User Ratings |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2405.06306 |