Business Intelligence Dashboard Using Power BI for Sentiment Analysis and Tokopedia Product Performance
Abstract
Customer reviews on e-commerce platforms provide valuable insights into product performance and consumer satisfaction. However, most existing studies focus primarily on sentiment classification and model evaluation, with limited integration of sentiment data and business indicators such as product price, sales volume, and category. This study developed a Business Intelligence (BI) dashboard using Microsoft Power BI to analyze Tokopedia product performance by integrating customer sentiment, ratings, product prices, categories, review counts, and sold counts. A descriptive quantitative approach was employed using a secondary dataset consisting of 65,543 reviews, 5,521 products, 856 anonymous shops, and 13 attributes. Sentiment labels were transformed into numerical sentiment scores (positive = 1, neutral = 0, negative = −1) through a rule-based mapping method to support quantitative analysis. The research process included data inspection, preprocessing, sentiment score transformation, data modeling, dashboard development, correlation analysis, and functional evaluation. Results showed that positive sentiment dominated the dataset, accounting for 97.56% of all reviews. The Food and Beverage category recorded the highest review volume and average sold count, while Electronics had the highest average product price. Spearman correlation analysis revealed a moderate negative relationship between product price and sold count (−0.443) and a strong positive relationship between review count and sold count (0.722). The dashboard supports data-driven product evaluation, pricing, and marketing decisions.
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References
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