Classification of Fake Product Ratings Using a Timeline Based Approach
Abstract— Detection of fake review and reviewers is currently a challenging problem in cyber space. It is challenging primarily due to the dynamic nature of the methodology used to fake the review. There are several aspects to be considered when analyzing reviews to classify them effective into genuine and fake. Sentiment analysis, opinion mining and intend mining are fields of research that try to accomplish the goal through Natural Language Processing of the text content of the review. In this paper, an approach that uses the review ratings evaluated along a timeline is presented. An Amazon dataset comprising of ratings indicated for a wide range of products was used for the analysis presented here. The analysis of the ratings was carried out for an electronic product over a period of six years. The computed average rating helps to identify linear classifiers that define solution boundaries within the dataspace. This enables a product specific classification of review ratings and suitable recommendations can also be generated automatically. The paper explains a methodology to evaluate the average product ratings over time and presents the research outcomes using a novel classification tool. The proposed approach helps to determine the optimal point to distinguish between fake and genuine ratings for each product.
Index Terms: Fake reviews, Fake Ratings, Product Ratings, Online Shopping, Amazon Dataset.
Copyright & License
All Research Plus Journals (RPJ) publish open access articles under the terms of the Creative Commons Attribution (CC BY-SA 4.0) https://creativecommons.org/licenses/by-sa/4.0/ License which permits use, distribution and reproduction in any medium, provided the original work is properly cited & ShareAlike terms followed.
Copyright on any research article in a journal published by a RPJ is retained by the author(s). Authors grant RPJ a license to publish the article and identify itself as the original publisher. Upon author(s) by giving permission to RPJ either via RPJ journal portal or other channel to publish their research work in RPJ agrees to all the terms and conditions of https://creativecommons.org/licenses/by-sa/4.0/ License and terms & condition set by RPJ.
3rd party copyright
It is the responsibility of author(s) to secure all necessary copyright permissions for the use of 3rd-party materials in their manuscript.
Research Plus Journals Open Access articles posted to repositories or websites are without warranty from RPJ of any kind, either express or implied, including, but not limited to, warranties of merchantability, fitness for a particular purpose, or non-infringement. To the fullest extent permitted by law RPJ disclaims all liability for any loss or damage arising out of, or in connection, with the use of or inability to use the content.