Implementation of multiple NLP approaches, in order to identify fake product reviews at one of the leader Virtual Merchants of the world.
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Updated
Aug 14, 2021 - Jupyter Notebook
Implementation of multiple NLP approaches, in order to identify fake product reviews at one of the leader Virtual Merchants of the world.
Successfully developed a machine learning model which can predict whether an online review is fraudulent or not. The main idea used to detect the fake nature of reviews is that the review should be computer generated through unfair means. If the review is created manually, then it is considered legal and original.
Part of code of my MSc Thesis with title "Application of Machine Learning Algorithms to Social Media Analysis"
Android app for spam and fake review detection.
FakeChecker is a part of my Engineering thesis project on Warsaw University of Technology. Its aim is to detect fake reviews on Google Maps.
This project related to my MSc Thesis that investigates the influence of linguistic and sentiment analysis features on detecting fake reviews in e-commerce (Amazon).
This project related to one of my B.Tech final year project that investigates the influence of linguistic and sentiment analysis features on detecting fake reviews in e-commerce (Amazon).
A Fraud detection extension.
Natural Language Processing | BRACU
Fake review detection using machine learning and deep learning techniques such as CNNs, SOMs, K-means clustering, various supervised models and natural language processing tools such as Word2Vec & TFIDF, GloVe etc.
Fake Review detection on YELP dataset
Undergraduate thesis project on pothole detection using crowd sourced reviews and smartphone sensor data and computer vision
Project Files for Final Year Project
Bengali/Bangla Fake Review Detection Dataset
This is my final year project "customer reviews classification and analysis system using data mining and nlp". It analyzes and then classifies the customer reviews on the basis of their fakeness, sentiments, contexts and topics discussed. The reviews are taken from various e-commerce platforms like daraz and amazon.
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