<![CDATA[The rapid growth of e-commerce in Indonesia has increased the importance of consumer reviews as signals influencing purchasing decisions. This study examines the relationship between review sentiment and sales performance in the carpentry tools category on Tokopedia. Using a 2019 Kaggle dataset consisting of 1,826 reviews across approximately 60 products, we apply an NLP-based pipeline to classify review sentiment into positive, neutral, and negative categories. Sentiment labeling combines rating-based rules and a TF-IDF + Logistic Regression baseline, with additional evaluation using IndoBERT. Product-level metrics—including the proportion of positive sentiment (pos_share), average rating, and units_sold (sales proxy)—are analyzed using descriptive statistics, correlation analysis, and cross-sectional OLS regression. The findings reveal that, in this snapshot dataset, the association between positive sentiment share and log(units_sold + 1) is weak and statistically limited, suggesting that sales variation cannot be explained solely by sentiment polarity or average ratings without considering other commercial factors. These results highlight the importance of incorporating contextual variables and temporal design in future research. Practically, the study suggests that sellers should monitor not only sentiment polarity but also the informational richness of reviews to strengthen reputation management strategies.]]>
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