DeepSpamReview: Detection of Fake Reviews on Online Review Platforms using Deep Learning Architectures. Summer Internship project at CoreView Systems.
-
Updated
Apr 3, 2022 - Jupyter Notebook
DeepSpamReview: Detection of Fake Reviews on Online Review Platforms using Deep Learning Architectures. Summer Internship project at CoreView Systems.
FakeChecker is a part of my Engineering thesis project on Warsaw University of Technology. Its aim is to detect fake reviews on Google Maps.
Spam Slayer is a web app used to detect deceptions from Amazon products' reviews. Users can copy a link to the product and paste directly paste it to my page. Using my trained deep learning model, Spam Slayer will distinguish between true/fake reviews and show them an adjusted rating without fake reviews.
Identifying fake reviews on Sephora using One Class SVM
Case Study from CSE380
Creating an algorithm with Tensorflow to detect fake reviews
Identifying fake reviews on Sephora using MLP autoencoder with anomaly detection
Detects fake product reviews using supervised ML algorithms like SVM, Random Forest, and XGBoost. Uses NLP techniques (tokenization, lemmatization, TF-IDF) for preprocessing. SVM achieved the highest accuracy and F1-score. Aims to enhance trust in online review systems.
A Project about Predicting the Number of Fake Reviews with Grades of an e-commerce website called Amazon using Machine Learning Models like Bert & XLnet.
Fake review detection in Yelp dataset
NetSpam – Academic project on spam detection in online shopping reviews
Add a description, image, and links to the fake-reviews topic page so that developers can more easily learn about it.
To associate your repository with the fake-reviews topic, visit your repo's landing page and select "manage topics."