ItemArticle

Deep learning based model for computing percentage of fake in user reviews using topic modelling techniques

Title
Deep learning based model for computing percentage of fake in user reviews using topic modelling techniques
Creator
K S S.; Danti A.; Snehitha Reddy T.; Nath V.
Description
Sentiment analysis plays a vital role in real time environment for knowing the history of a product or any other specific entity. Due to large number of users in the www, chances are there that many fake users may upload the fake reviews to damage the business for the sake of money. Identifying the fake reviews or percentage of fake content in the review is yet a challenging task. In this paper, an attempt has been made to find the percentage of fake in the review data. Two methodologies are combined to address this issue. Concept of spelling checking, topic modelling and deep learning for context extraction is extensively used to build the effective model. Proposed technique is exhaustively checked for efficiency with many trails of experiments. Also, the training and testing samples were shuffled for experimentation. The results of the models show its goodness. The details of the results can be found at experiments section. 2024 The Author(s)
Source
e-Prime - Advances in Electrical Engineering, Electronics and Energy, Vol-8
Date
2024-01-01
Publisher
Elsevier Ltd
Subject
Challenging task; Deep learning; Exhaustivity; Fake review detection; Topic Modelling
Coverage
K S S., Dept of MCA, PESITM, SHIMOGA, India; Danti A., Christ university, Bangalore, India; Snehitha Reddy T., Research Scholar Department of Electronics & Communication Engineering Birla Institute of Technology, JH, Mesra Ranchi, 835215, India; Nath V., VLSI Design Group, Department of ECE, B.I.T. Mesra, (JH), Ranchi, 835215, India
Rights
All Open Access; Gold Open Access
Relation
ISSN: 27726711
Format
Online
Language
English
Type
Article

Collection

Articles

Citation

K S S.; Danti A.; Snehitha Reddy T.; Nath V., “Deep learning based model for computing percentage of fake in user reviews using topic modelling techniques,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 25, 2025, https://archives.christuniversity.in/items/show/13104.

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