Extracting Advertising Elements and the Voice of Customers in Online Game Reviews

Abstract

The growth of electronic word-of-mouth (eWOM) on digital platforms has heightened the need to distinguish authentic user-generated content from covert promotional material. This study proposes an integrated framework combining Natural Language Processing (NLP), machine learning, and Latent Dirichlet Allocation (LDA) to classify sentiment and detect advertising features in online game reviews. Reviews from the Steam platform were analyzed using Support Vector Machine (SVM), Decision Tree, and Naïve Bayes classifiers, with class imbalance addressed through SMOTE and SMOTE–Tomek techniques. The SMOTE-augmented SVM achieved the highest performance, with 98.18% overall accuracy and 97.52% negative sentiment detection. LDA and Quality Function Deployment (QFD) further uncovered latent promotional themes, providing insights into how advertising elements manifest in positive reviews and how negative feedback reflects genuine user concerns. The framework assists platform managers in enhancing eWOM credibility and supports marketers in designing data-driven advertising strategies. By bridging sentiment analysis with covert marketing detection, this research contributes a novel methodological approach for assessing review trustworthiness, improving transparency, and fostering consumer trust in digital information environments.