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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.
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.
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).
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.
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.
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).