Fake Review Detection From a Product Review Using Modified Method of Iterative Computation Framework

Abstract

The rapid growth of the Internet influenced many of our daily activities. One of the very rapid growth area is ecommerce. Generally e-commerce provide facility for customers to write reviews related with its service. The existence of these reviews can be used as a source of information. For examples, companies can use it to make design decisions of their products or services, while potential customers can use it to decide either to buy or to use a product. Unfortunately, the importance of the review is misused by certain parties who tried to create fake reviews, both aimed at raising the popularity or to discredit the product. This research aims to detect fake reviews for a product by using the text and rating property from a review. In short, the proposed system (ICF++) will measure the honesty value of a review, the trustiness value of the reviewers and the reliability value of a product. The honesty value of a review will be measured by utilizing the text mining and opinion mining techniques. The result from the experiment shows that the proposed system has a better accuracy compared with the result from iterative computation framework (ICF) method.

Spam Detection Framework Using Sentimenatl Analysis

“…Likewise make a system of clients, audits and things and utilize essential suppositions (for instance an analyst is more dependable on the off chance that he/she composes more genuine reviews) and mark surveys. Wahyuni [8] proposes a half breed strategy for spam discovery utilizing a calculation called ICF++ which is an augmentation to ICF of in which simply audit rating are utilized to find spam identification. This work utilize likewise opinion investigation to accomplish better precision specifically…”

Online Products Fake Reviews Detection System Using Machine Learning

“…In [8] paper, to identify counterfeit audits for an item by utilizing the content and rating property from a survey. To put it plainly, suggested framework (ICF++) quantify genuineness estimation survey, trustiness estimation analysts plus unwavering quality estimation item.…”

Deceptive consumer review detection: a survey

“…Table 8 summarises the research which followed association rule-based mining approaches, social relationship analysis approaches and multi-task learning approaches used to detect deceptive reviews. Wahyuni and Djunaidy (2016) stated that the precision value of rulebased ICF++ is higher than ICF, while Lai et al (2010b) proposed an inferential language model which outperformed all the other methods in TREC-like experiment, with a large dataset downloaded from Amazon. The trust-based rating predictions-RWR algorithm achieved a higher accuracy than standard Computation Framework (CF) method and a strong correlation between social relationships and the computed trustworthiness scores (Xue et al 2015).…”