Identification of Misleading Reviews from Textual Content Using Feature Structure with Machine Learning Model

The misleading reviews posted on shopping websites and other media platforms sway the opinions and decisions of different customers. On the other hand, dishonest reviewers will make an effort to mimic the writing style of legitimate reviews. There is no guarantee that these text-feature-based approaches will work anytime soon. In addition, the likelihood of an imbalanced category distribution in practice limits detection performance. This paper proposes a fraudulent review detection system that uses ensemble feature selection and multidimensional feature creation to overcome these limitations. Our idea builds three-dimensional characteristics, which include text, reviewer behaviour, and misleading scores. Furthermore, a data resampling approach combines Random Sampling and oversampling techniques to mitigate the effects of an imbalanced distribution of categories. In addition, we combine the outcomes of several feature selection methods that focus on information gain, XGBoost feature importance, and the Chi-square test. On various text datasets, the proposed technique demonstrates exemplary performance in fraudulent review identification according to the experimental findings using feature selection methods, resampling methods, classification, etc. Our technique outperforms existing sophisticated methods when faced with low-quality text or an imbalanced dataset.