Spam Review Classification Using Ensemble of Global and Local Feature Selectors

Abstract

In our work, we propose an ensemble of local and global filter-based feature selection method to reduce the high dimensionality of feature space and increase accuracy of spam review classification. These selected features are then used for training various classifiers for spam detection. Experimental results with four classifiers on two available datasets of hotel reviews show that the proposed feature selector improves the performance of spam classification in terms of well-known performance metrics such as AUC score.

Analysis and Challenges in Detecting the Fake Reviews of Products using Naïve Bayes and Random Forest Techniques

“…Another spam review classification method [8] The ensemble classification method [11] detected the fake reviews for Amazon Mechanical Turk (AMT) and Trip advisor datasets. Further, it evaluated the system using 5-fold cross-validation.…”

“…Extend with any other domain dataset of spam detection. 13 Extend with meta-features and textual features for performance improvement [8].…”

“…Automated fake review identification, detection, analysis, and prediction becomes an important zone of sentiment analysis in the present scenario of Natural Language Processing (NLP) and the E-commerce market. Recently, the requirement of these automatic systems using Machine Learning (ML) techniques has grown greatly [1] - [8] that applies a strong base of NLP, ML, and Artificial Intelligence (AI) fields.…”