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fake-review-detection

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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.

  • Updated Apr 14, 2022
  • Jupyter Notebook

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.

  • Updated Oct 25, 2024
  • Jupyter Notebook

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.

  • Updated Feb 27, 2024

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