Detecting truthful and useful consumer reviews for products using opinion mining

Kalpana Algotar, Ajay Bansal

Research output: Contribution to journalConference article

Abstract

Individuals and organizations rely heavily on social media these days for consumer reviews in their decision-making on purchases. However, for personal gains such as profit or fame, people post fake reviews to promote or demote certain target products as well as to deceive the reader. To get genuine user experiences and opinions, there is a need to detect such spam or fake reviews. This paper presents a study that aims to detect truthful, useful reviews and ranks them. An effective supervised learning technique is proposed to detect truthful and useful reviews and rank them, using a 'deceptive' classifier, 'useful' classifier, and a 'ranking' model respectively. Deceptive and non-useful consumer reviews from online review communities such as amazon.com and Epinions.com are used. The proposed method first uses the 'deceptive' classifier to find truthful reviews followed by the 'useful' classifier to find whether a review is useful or not. Manually labeling individual reviews is very difficult and time consuming. We incorporate a dictionary that makes it easy to label reviews. We present the experimental results of our proposed approach using our dictionary with 'deceptive' classifier and 'useful' classifier.

Original languageEnglish (US)
Pages (from-to)63-72
Number of pages10
JournalCEUR Workshop Proceedings
Volume2111
StatePublished - Jan 1 2018
Event4th Workshop on Sentic Computing, Sentiment Analysis, Opinion Mining, and Emotion Detection, EMSASW 2018 - Heraklion, Greece
Duration: Jun 4 2018 → …

Keywords

  • Opinion mining
  • Spam review detection
  • Supervised learning
  • Text classification

ASJC Scopus subject areas

  • Computer Science(all)

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