A Novel Hybrid Feature Extraction Technique and Spam Review Detection using Ensemble Machine Learning Algorithm by Web Scrapping

Abstract

Objectives: To develop a novel hybrid method for feature generation and a novel dataset for experimenting and extracting the features for numerical representation. Methods: In the pursuit of the best spam review detection model, a four-stage process was undertaken. Initially, a dataset 'Fake reviews' was collected from Flipkart, containing 9926 samples from the home and kitchen products domain. Next, the data underwent pre-processing using the Natural Language Toolkit (NLTK) library. A novel Hybrid Feature Generator (HFG) was then developed, extracting informative features based on parameters like TF-IDF (Term Frequency -Inverse Document Frequency), sentiment analysis scores, and syntactic patterns. Finally, the model was trained on these generated features using Gaussian Naïve Bayes (GNB), Multinomial Naïve Bayes (MNB), and Bernoulli Naïve Bayes (BNB) algorithms. Performance evaluation was conducted using metrics such as accuracy, precision, recall, and F1-score, comparing the model's results to gold standard or known spam reviews. Findings: The feature generation technique was implemented on three different models, and the models were trained using 70% of the available data. The results of these experiments showed that GNB, NB, and NB achieved testing accuracies of 99.7%, 96.4%, and 99%, respectively. The performance of these models was compared with and without the inclusion of extracted product review features. The results demonstrated that the GNB algorithm outperformed the other methods in terms of accuracy and precision. Novelty: This study presents a novel HFG for feature extraction from review-text and a novel dataset that outperforms hitherto reported https://www.indjst.org/ approaches.

Enhancing Spam Email Classification Using Effective Preprocessing Strategies and Optimal Machine Learning Algorithms

“…Goyal et al used three supervised machine learning methods: Gaussian Naïve Bayes (GNB), Multinomial Naïve Bayes, and Bernoulli Naïve Bayes to detect fake reviews. The GNB classifier outperforms other models in terms of accuracy and F1-score metric, as well as identifying deceptive reviews (7) . The authors used the NLTK library to clean up the review data, which is a predefined functionality, so the pre-processing methodology is not novel.…”