Fake Research Detection using Weighting Algorithm in Netspam Framework
Chaitrali S. Kardile1, Revati M. Wahul2

1Chaitrali S. Kardile, Student, MESCOE, Pune, Maharashtra, India Bharath Institute of Higher Education and Research, Chennai (Tamil Nadu), India.
2Revati M. Wahul, Assistant Professor, MESCOE, Pune (Maharashtra), India.
Manuscript received on 12 October 2019 | Revised Manuscript received on 21 October 2019 | Manuscript Published on 02 November 2019 | PP: 831-835 | Volume-8 Issue-2S11 September 2019 | Retrieval Number: B11360982S1119/2019©BEIESP | DOI: 10.35940/ijrte.B1136.0982S1119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Now a day’s our life has become more dependent on social media. Social has opened many opportunity for business so, whenever customer wants to buy new product they will look for other people’s opinion. Social media has also have major drawback for business strategies which is spammers. Spammers create spam surveys about various products which mislead a consumer. This online opinion plays important role in business strategies, while positive opinion gives good publicity and market on the other side negative opinion gives bad publicity and market which affects the service providers. To avoid this spammers there have been many research but very have work on user and review related feature. In this investigation we propose a classification system using heterogeneous information network NetSpam framework. This system will classify spam and non-spam reviews using NetSpam algorithm and naïve bayes classifier for sentiment analysis which will provide positive and negative value of the product review. And furthermore if wants to search top product, user can use search query, in addition to that it will display recommendation product on the basis of user’s point of interest.
Keywords: Social Media, Amazon API, Spammer, Spam Review, Heterogeneous Information Networks, Naive Bayes, Metapath.
Scope of the Article: Patterns and Frameworks