Attention-based Bidirectional LSTM for Deceptive Opinion Spam Classification
This addresses the issue of fraudulent reviews misleading consumers in e-commerce, though it is an incremental improvement over existing methods.
The paper tackled the problem of detecting deceptive opinion spam in online reviews by proposing an Attention-based Bidirectional LSTM model, which achieved improved classification performance compared to baseline machine learning techniques.
Online Reviews play a vital role in e commerce for decision making. Much of the population makes the decision of which places, restaurant to visit, what to buy and from where to buy based on the reviews posted on the respective platforms. A fraudulent review or opinion spam is categorized as an untruthful or deceptive review. Positive reviews of a product or a restaurant helps attract customers and thereby lead to an increase in sales whereas negative reviews may hamper the progress of a restaurant or sales of a product and thereby lead to defamed reputation and loss. Fraudulent reviews are deliberately posted on various online review platforms to trick customers to buy, visit or distract against a product or a restaurant. They are also written to commend or discredit the product's repute. The work aims at detecting and classifying the reviews as deceptive or truthful. It involves use of various deep learning techniques for classifying the reviews and an overview of proposed approach involving Attention based Bidirectional LSTM to tackle issues related to semantic information in reviews and a comparative study over baseline machine learning techniques for review classification.