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).

Our study utilizes BERT and LSTM models alongside Monte Carlo Dropout (MCD) on the Yelp Labelled Dataset. MCD bolsters robustness by introducing uncertainty through neuron dropout. The BERT-embedded MCD achieves an impressive 91.75% accuracy, surpassing the LSTM model.

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

Fraud, Fake News, Fake Review, Detection using AI in a extention.

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.

Fake Review Detection System Using Machine Learning with source code and documents. It is full stack web application project with code and documents.