Improving Fake Review Detection in E-commerce using Combined Analysis Techniques

Rao, Shreyas Akash (2024) Improving Fake Review Detection in E-commerce using Combined Analysis Techniques. Masters thesis, Dublin, National College of Ireland.

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Abstract

The problem of fake reviews has become widespread and is a constant threat to most e-commerce platforms impacting the consumer and business world. The research question of this study is as follows, “How can detecting fake reviews in e-commerce be improved using combined analysis techniques?” The dataset used in this study is obtained from Kaggle; this comprises numeric features like ratings, helpfulness votes, and polarity scores from a sentiment analysis of the text content. Two modeling scenarios were explored: The focus on imbalanced data and the outcome of numeric features on balanced data. The use of techniques to resample the dataset led to an overwhelming increase in minority class detection (unverified reviews). The Random Forest/Decision Tree model had 94% accuracy and the Gradient boosting/ XGBoost model had 87% and 94% accuracy respectively. However, there are limitations to the generalization of fake reviews where the approach is reported to have low precision. The study confirms that numeric and text features are promising for fake review identification and underscores the future directions in feature selection, feature combination as well as algorithm fine-tuning. These findings offer implications for Theory development and practical use in highlighting the significance of the proper anti-fraud mechanisms in e-commerce structures.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Horn, Christian UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science P Language and Literature > P Philology. Linguistics > Computational linguistics. Natural language processing H Social Sciences > HF Commerce > Electronic Commerce Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	04 Sep 2025 13:13
Last Modified:	04 Sep 2025 13:13
URI:	https://norma.ncirl.ie/id/eprint/8787

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