Fake Product Review Monitoring & Removal and Sentiment Analysis of Genuine Reviews

  • Abhishek Punde
  • Sanchit Ramteke
  • Shailesh Shinde
  • Shilpa Kolte
Keywords: Sentimental Analysis, Decision Tree Classifier, Native Bayes Classifier, Tokenization, Bag-of-words Tagging, Fake Reviews, Genuine Reviews

Abstract

Any E-Commerce website gets bad reputation if they sell a product which has bad review, the user blames the e- Commerce website rather than manufacturers most of the times. In some review sites some great audits are included by the item organization individuals itself so as to make so as to deliver false positive item reviews. To eliminate these type of fake product review, we will create a system that finds out the fake reviews and eliminates all the fake reviews by using machine learning. We also remove the reviews that are flood by a marketing agency in order to boost up the ratings of a particular product .Finally Sentiment analysis is done for the genuine reviews to classify them into positive and negative. We will use Bag-of-words to label individual words according to their sentiment.

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References

Heydari, A., Ali Tavakoli, M., Salim, N., & Heydari, Z. (2015). Detection of review spam: A survey. Expert Systems with Applications, 42(7), 3634–3642.

Lau, R. Y., Liao, S., Kwok, R. C. W., Xu, K., Xia, Y., & Li, Y. (2011). Text mining and probabilistic language modeling for online review spam detecting. ACM Transactions on Management Information Systems, 2(4), 1–30.

Ramkumar, V., Rajasekar, S., & Swamynathan, S. (2010). Scoring products from reviews through application of fuzzy techniques. Expert Systems with Applications, 37(10), 6862–6867.

Heydari, A., Tavakoli, M., & Salim, N. (2016). Detection of fake opinions using time series. Expert Systems with Applications, 58, 83-92.

Published
2019-04-30
How to Cite
Abhishek Punde, Sanchit Ramteke, Shailesh Shinde, & Shilpa Kolte. (2019). Fake Product Review Monitoring & Removal and Sentiment Analysis of Genuine Reviews. International Journal of Engineering and Management Research, 9(2), 107-110. https://doi.org/10.31033/ijemr.9.2.12
Section
Articles