Spotting Fake Reviews using Positive-Unlabeled Learning
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| Format: | Artículo científico |
| Language: | en |
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Instituto Politécnico Nacional
2014
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| _version_ | 1876428647888322560 |
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| author | Huayi Li |
| author_facet | Huayi Li |
| contents | Spotting Fake Reviews using Positive-Unlabeled Learning Huayi Li Bing Liu Jidong Shao Arjun Mukherjee Computación PU Positive learning Fake reviews Unlabeled learning Fake review detection has been studied by researchers for several years. However, so far all re- ported studies are based on English reviews. This paper reports a study of detecting fake reviews in Chinese. Our review dataset is from the Chinese review hosting site Di- anping 1 , which has built a fake review detection system. They are confident that their algorithm has a very high precision, but they don’t know the recall. This means that all fake reviews detected by the system are almost certainly fake but the remaining reviews may not be all genuine. This paper first reports a supervised learning study of two classes, fake and unknown. However, since the unknown set may contain many fake reviews, it is more appropriate to treat it as an unlabeled set. This calls for the model of learning from positive and unla- beled examples (or PU-learning). Experimental results show that PU learning not only outperforms supervised learning significantly, but also detects a large number of potentially fake reviews hidden in the unlabeled set that Dianping fails to detect. 2014 artículo científico 1405-5546 https://www.redalyc.org/articulo.oa?id=61532067005 en http://www.redalyc.org/revista.oa?id=615 Computación y Sistemas application/pdf Instituto Politécnico Nacional Computación y Sistemas (México) Num.3 Vol.18 |
| format | Artículo científico |
| id | redalyc_61532067005 |
| institution | Redalyc |
| language | en |
| publishDate | 2014 |
| publisher | Instituto Politécnico Nacional |
| spellingShingle | Spotting Fake Reviews using Positive-Unlabeled Learning Huayi Li Computación PU Positive learning Fake reviews Unlabeled learning Spotting Fake Reviews using Positive-Unlabeled Learning Huayi Li Bing Liu Jidong Shao Arjun Mukherjee Computación PU Positive learning Fake reviews Unlabeled learning Fake review detection has been studied by researchers for several years. However, so far all re- ported studies are based on English reviews. This paper reports a study of detecting fake reviews in Chinese. Our review dataset is from the Chinese review hosting site Di- anping 1 , which has built a fake review detection system. They are confident that their algorithm has a very high precision, but they don’t know the recall. This means that all fake reviews detected by the system are almost certainly fake but the remaining reviews may not be all genuine. This paper first reports a supervised learning study of two classes, fake and unknown. However, since the unknown set may contain many fake reviews, it is more appropriate to treat it as an unlabeled set. This calls for the model of learning from positive and unla- beled examples (or PU-learning). Experimental results show that PU learning not only outperforms supervised learning significantly, but also detects a large number of potentially fake reviews hidden in the unlabeled set that Dianping fails to detect. 2014 artículo científico 1405-5546 https://www.redalyc.org/articulo.oa?id=61532067005 en http://www.redalyc.org/revista.oa?id=615 Computación y Sistemas application/pdf Instituto Politécnico Nacional Computación y Sistemas (México) Num.3 Vol.18 |
| title | Spotting Fake Reviews using Positive-Unlabeled Learning |
| topic | Computación PU Positive learning Fake reviews Unlabeled learning |
| url | https://www.redalyc.org/articulo.oa?id=61532067005 |