Understanding Aspect-Sentiment Drivers of Overall Ratings in Second Hand Marketplace Apps through Text Analytics and Regression Analysis

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Abstract
User-generated reviews and star ratings strongly influence customer trust, download decisions, and platform reputation in mobile app marketplaces. This study analyzes reviews from three Turkish secondhand marketplace applications (Letgo, Dolap, Gardrops) on Google Play to uncover how specific aspect–sentiment patterns affect overall ratings.A multi-stage natural language processing (NLP) pipeline was applied. Review sentences were embedded with multilingual SBERT and clustered using KMeans, resulting in 13 higher level aspect categories. Sentiment was classified using a domain specific Turkish ELECTRA model validated on 1,000 manually annotated sentences. Ridge regression was then employed to quantify the contribution of aspect–sentiment pairs to star ratings. The analysis showed that negative experiences related to returns, shipping costs and fraudulent practices consistently decreased ratings, while positive mentions of usability, transaction satisfaction and customer support produced stronger rating improvements. Comparative findings revealed that higher-rated apps (Dolap: 4.4, Gardrops: 4.2) accumulated more positive experiences, whereas Letgo (2.7) exhibited recurring trust and fairness issues. These results highlight which service aspects most strongly shape customer evaluations. By linking aspect-level sentiment to rating outcomes, the study provides platform managers with actionable insights for improving satisfaction and strengthening competitiveness in secondhand C2C marketplaces.

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