Open AccessProceedings Article
Negative Deceptive Opinion Spam
Myle Ott,Claire Cardie,Jeffrey T. Hancock +2 more
- pp 497-501
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TLDR
This work creates and study the first dataset of deceptive opinion spam with negative sentiment reviews, and finds that standard n-gram text categorization techniques can detect negative deceptive opinions spam with performance far surpassing that of human judges.Abstract:
The rising influence of user-generated online reviews (Cone, 2011) has led to growing incentive for businesses to solicit and manufacture DECEPTIVE OPINION SPAM—fictitious reviews that have been deliberately written to sound authentic and deceive the reader. Recently, Ott et al. (2011) have introduced an opinion spam dataset containing gold standard deceptive positive hotel reviews. However, the complementary problem of negative deceptive opinion spam, intended to slander competitive offerings, remains largely unstudied. Following an approach similar to Ott et al. (2011), in this work we create and study the first dataset of deceptive opinion spam with negative sentiment reviews. Based on this dataset, we find that standard n-gram text categorization techniques can detect negative deceptive opinion spam with performance far surpassing that of human judges. Finally, in conjunction with the aforementioned positive review dataset, we consider the possible interactions between sentiment and deception, and present initial results that encourage further exploration of this relationship.read more
Citations
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Journal ArticleDOI
Product review management software based on multiple classifiers
Cagatay Catal,Suat Guldan +1 more
TL;DR: A model using a multiple classifier system to identify deceptive negative customer reviews is proposed, validated with a dataset of hotel reviews from TripAdvisor and provided remarkable results that demonstrate improvement upon approaches reported in the literature.
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360 degree view of cross-domain opinion classification: a survey
TL;DR: An organized survey of SA (also known as opinion mining) containing approaches, datasets, languages, and applications used is presented to support researches to get a greater understanding on emerging trends and state-of-the-art methods to be applied for future exploration.
Proceedings ArticleDOI
An ensemble approach to detect review spam using hybrid machine learning technique
TL;DR: An ensemble learning approach which combines two different types of learning methods (active and supervised) by creating a hybrid dataset of both real-life and pseudo reviews which achieves phenomenal results while working on almost 3600 reviews from different domains.
Journal ArticleDOI
Text Analysis in Adversarial Settings: Does Deception Leave a Stylistic Trace?
Tommi Gröndahl,Nadarajah Asokan +1 more
TL;DR: Current style transformation methods fail to achieve reliable obfuscation while simultaneously ensuring semantic faithfulness to the original text, and it is proposed that future work in style transformation should pay particular attention to disallowing semantically drastic changes.
Book ChapterDOI
Review Spam Detection Using Opinion Mining
TL;DR: This paper has applied supervised learning technique to detect review spam and uses different set of features along with sentiment score to build models and their performance were evaluated using different classifiers.
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