Deceptive opinion spam detection using feature reduction techniques

People usually prepare themselves by reading online reviews before purchasing a product. Sellers sometimes try to imitate user experience as a deceptive review to increase profits. Deceptive opinion spam detection has emerged as a challenging task in the field of opinion mining. Feature reduction techniques play the most important role in data mining which finds the essential features and removes the unnecessary dimensions that only contribute to the noise. This article extracts various textual features of gold-standard deceptive hotel reviews using different representation techniques like Part of Speech tag (POS tag), Bag of Word (BoW), and Doc2Vec. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are applied to reduce the features' dimensions. Various supervised classifiers like Decision Tree (DT), Na¨ıve Bayes (NB), Logistic Regression (LR), and Support Vector Machine (SVM) are used to classify deceptive opinions and truthful opinions. The features used by these supervised classifiers cannot retain sequential information from reviews. To overcome this problem, we used the Words Attention-based Bidirectional Long Short-Term Memory (WABiLSTM) network model that trains to learn the patterns of words. The article examines machine and deep learning-based spam detection models and provides their outline and results. The metrics like accuracy, precision, recall, and F-Measure are used to analyze the performance of these classification models. The experimental results showed the model's performance improved after reducing the features.