• Fardeen Khan
• Dr Sunita Sachin Dhotre

The online market is rife with opinion spam that takes the appearance of reviews. People are frequently hired to write favourable or unfavourable evaluations for certain products in order to encourage or discourage sales of those products. A lot of times, groups work on this. Nothing has been done to uncover those who are targeting a complete brand rather than a specific product, despite earlier study attempts to identify and analyse this spam group.Reviews were collected from Amazon's product review website and painstakingly sorted into 923 different reviewer groups for this post. When groups are extracted using frequent itemset mining over brand similarities, users who have evaluated many different brands together are clustered together.It has been suggested that the composition of reviewer groups be determined by eight features unique to a (group, brand) pair. A feature-based supervised model has been developed to classify possible candidate groups as extremist entities. On the reviews that group members have supplied, a variety of classifiers have been run in order to determine whether a group demonstrates signs of extremism. A three-layer Perceptron system is discovered to be the highest accurate classifier. The inquiry has been carried out in order to thoroughly examine the actions of such organisations in order to better understand the dynamics of brand-level opinion fraud. Examples of these practises include consistency in rating, review sentiment, confirmed purchases, review dates, and favourable reviewer comments. Surprisingly, several authenticated reviews have been found to be expressing intense sentiments,Unexpectedly, it has been seen that a lot of qualified reviewers are voicing very strong sentiments. More investigation reveals ways to bypass Amazon's current measures to prevent unofficial incentives.

Java,LSTM,rnn algorithm, NLTK (natural language Tool Kit)

"Detecting and Characterizing Extrimist Reviewer Groups in Online Product Reviews", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 9, page no.e33-e46, September-2023, Available :http://www.jetir.org/papers/JETIR2309405.pdf

"Detecting and Characterizing Extrimist Reviewer Groups in Online Product Reviews", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 9, page no. ppe33-e46, September-2023, Available at : http://www.jetir.org/papers/JETIR2309405.pdf