Document Type : Original Research Manuscripts
Authors
1
Department of Computer Applications, RIMT University, Mandi Gobindgarh, Punjab, India.
2
Senior Research Fellow, UBS, Panjab University, Chandigarh, India.
3
Professor, SMS, CGC University, Mohali, Punjab, India.
4
Assistant Professor, UIC, Chandigarh University, Mohali, India.
10.22034/kps.2026.602711.1311
Abstract
While online review platforms are a significant factor in e-commerce decision-making, deceptive reviews can diminish the trustworthiness of review-based information and recommendations. In this research, an empirical model for detecting fake reviews and recommending trustworthy e-commerce is proposed based on traditional machine learning methods. The Deceptive Opinion Spam Corpus consists of 1600 hotel reviews, 800 of which are truthful and 800 of which are deceptive. The text is lowercased, unnecessary punctuation and special characters are removed, the text is tokenized, and the stop words are removed. Textual representation is done using TF-IDF with unigram and bigram features. The classifiers are trained on an 80:20 stratified split of the data with five-fold cross validation on the training set, and the resulting models are compared. The classifiers are trained using an 80:20 stratified split of the data, with five-fold cross validation on the training set, and the resulting models are compared. Models are assessed by accuracy, precision, recall, F1 score and ROC-AUC. SVM achieves the best overall performance, with 89.38% accuracy, 93.13% recall, 89.76% F1-score, and 0.9436 ROC-AUC. The deceptive probability predicted by it is transformed into a review trust score by the formula Trust(r) = 1 − P_fake(r). The review-level trust scores are then aggregated at the hotel level to facilitate the entity ranking based on trust, and the review polarity is provided as supporting information. The framework illustrates how the detection of fake reviews can be used to deliver a credibility signal for more trustworthy recommendation-oriented ranking.
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