ENHANCING PLATFORM CREDIBILITY IN THAI E-COMMERCE: RECALIBRATING INFLATED STAR RATINGS VIA SENTIMENT ANALYSIS

This study addresses the erosion of platform credibility in international e-commerce caused by the misalignment between numerical star ratings and actual textual sentiment. While star ratings are a dominant mechanism guiding purchasing decisions in rapidly expanding markets like Southeast Asia, they are frequently inflated due to platform-induced biases and region-specific cultural factors, such as the politeness norms and indirect communication strategies prevalent in Thai culture. To overcome this structural limitation, this research aims to empirically quantify this discrepancy and introduce a transparent, sentiment-informed metric—the "Heart Score"—to recalibrate ratings. A rule-based sentiment analysis framework specifically adapted for Thai-language reviews was applied to a dataset of 14,877 women’s clothing entries from Lazada. By leveraging expert-validated lexicons and linguistic rules for negation and amplification, store-level sentiment ratios were extracted and transformed into recalibrated scores via a normalized linear function with penalty adjustments. Findings confirm substantial sentiment misalignment: although the analyzed stores clustered narrowly with star ratings between 4.92 and 5.00, the recalibrated Heart Scores spanned a significantly wider, more meaningful range of 3.81 to 4.71. The most notable gap of 1.11 points exposed hidden dissatisfaction masked by near-perfect ratings. The proposed framework offers strong managerial insights for platform governance by enhancing review credibility, enabling more accurate product benchmarking, and providing a scalable, interpretable model for restoring trust in low-resource language environments.