Abstract
The outbreak of Covid-19 and the enforcement of lockdown, social distancing, and other precautionary measures lead to a global increase in online shopping. The increasing significance of online shopping and extensive use of e-commerce has increased competition between companies for online selling. Highlights that online reviews play a significant role in boosting a business or slandering it. Product review is an essential factor in customers’ decision-making, leading to an intense topic known as fraudulent or fake reviews detection. Given these reviews’ power over a business, the treacherous acts of giving false reviews for personal gains have increased with time. In our research, we proposed a fake review detection model by using Text Classification and techniques related to Machine Learning. We used classifiers such as Support Vector Machine, K-Nearest Neighbor, and logistic regression (SKL), using a bigram model that detects fraudulent reviews based on the number of pronouns, verbs, and sentiments. Our proposed methodology for detecting fake online reviews outperforms on the yelp dataset and the TripAdvisor dataset compared to other state-of-the-art techniques with 95% and 89.03% accuracy.
Highlights
T HE global pandemic of Covid-19 at the start of the year 2020 leaves a significant impact on everything and everyone
Atefeh et al [28] advised a robust spam review detection system to investigate suspicious time intervals of the online reviewers using time series by pattern recognition technique where the results show it to be a better, easy, and more straightforward approach as it gives an F-score of 86% as compared to others [28]
The results show that Support Vector Machine (SVM) has the highest recall, which means that the prediction process is efficient in SVM
Summary
T HE global pandemic of Covid-19 at the start of the year 2020 leaves a significant impact on everything and everyone This outbreak shakes the world and shifts the dynamics of e-commerce and online shopping. Dissimilar to other spam, opinion spam are a tad hard to detect as understanding the context is important to detect the deceptiveness of a review. In a study, [7] it was accentuated by the researchers that fake or genuine reviews are hard to label by humans. This complicates the search for the ground truth for given instances accurately. Social media consists of billions of short informal texts that may in-
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