Open AccessExamining and Predicting Helpfulness of reviews based on Naive Bayes
Author(s) -
M. D. Anto Praveena,
A. Christy,
L. Suji Helen,
Rahul Krishna,
Durgesh Nandini
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1770/1/012021
Subject(s) - helpfulness , asset (computer security) , psychology , feeling , systematic review , computer science , social psychology , political science , medline , computer security , law
Online reviews give a significant asset to potential clients to settle on buy choices. Be that as it may, the utmost extent of accessible reviews just as the enormous varieties in the review standard present a major hindrance to the compelling utilization of the reviews, as the extreme accommodating reviews might be covered in the huge measure of poor-standard reviews. The objective of this framework is to create models and calculations for anticipating the supportiveness of reviews, which gives the theorize for finding the extreme accommodating reviews for given items. We initially exhibit that the supportiveness of reviews depending upon the three significant variables: the commentator mastery, the composing approach of review, and the practicality of reviews. Contrasted with different all around examined conclusion investigation and feeling rundown issues, less exertion has been made to break down the nature of online reviews. The target of this paper is to fill right now naturally assessing the “supportiveness” of reviews and subsequently creating novel models to distinguish the most accommodating reviews for a specific item.