Many individuals and businesses make decisions based on freely and easily accessible online reviews. This provides incentives for the dissemination of fake reviews, which aim to deceive the reader into having undeserved positive or negative opinions about an establishment or service. With that in mind, this work proposes machine learning applications to detect fake online reviews from hotel, restaurant and doctor domains. In order to _lter these deceptive reviews, Neural Networks and Support Vector Ma- chines are used. Both algorithms' parameters are optimized during training. Parameters that result in the highest accuracy for each data and feature set combination are selected for testing. As input features for both machine learning applications, unigrams, bigrams and the combination of both are used. The advantage of the proposed approach is that the models are simple yet yield results comparable with those found in the literature using more complex models. The highest accuracy achieved was with Support Vector Machine using the Laplacian kernel which obtained an accuracy of 82.92% for hotel, 80.83% for restaurant and 73.33% for doctor reviews.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:du-28133 |
| Date | January 2018 |
| Creators | Ferreira Uchoa, Marina |
| Publisher | Högskolan Dalarna, Mikrodataanalys |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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