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Detecting Fake Reviews with Machine Learning
Dalarna University, School of Technology and Business Studies, Microdata Analysis.
2018 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

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.

Place, publisher, year, edition, pages
2018.
Keywords [en]
Text mining, review spam, fake review, deceptive review
National Category
Economics and Business
Identifiers
URN: urn:nbn:se:du-28133OAI: oai:DiVA.org:du-28133DiVA, id: diva2:1231410
Available from: 2018-07-06 Created: 2018-07-06

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CiteExportLink to record
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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
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  • asciidoc
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