Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
Nowadays, online marketplaces are frequently subjected to opinion spam in the form of reviews. Individuals are generally hired to encourage or sidetrack specific brands by writing highly positive or negative reviews. 1,2,3This is regularly done in groups. Although previous studies attempted to identify and analyze such opinion spam groups, little has been conducted in order to identify those groups which thus target a brand as a whole, rather than always products. We collected reviews from the Amazon product review site and manually labeled a set of 923 candidate reviewer groups for this application. Users are clustered together if they have mutually reviewed (products of) a large number of manufacturers, as determined by requent itemset mining over brand obvious parallels.
Keywords:
Behavior, electronic commerce, machine intelligence, machine learning, reviews, social computing, web mining.
Cite Article:
"IDENTIFYING AND DESCRIBING EXTREMIST REVIEWER GROUPS IN OPERATIONAL PRODUCT REVIEW", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.7, Issue 5, page no.613 - 617, May-2022, Available :http://www.ijrti.org/papers/IJRTI2205104.pdf
Downloads:
000205087
ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator