From LovBuy Reviews to Spreadsheets: Building a Reseller Product Reputation Analysis System
2025-07-13
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In today's competitive cross-border e-commerce landscape, analyzing product reviews has become crucial for resellers to identify market trends and consumer preferences. This article explores how to systematically collect, process, and analyze LovBuy review data to create an effective reputation analysis system.
1. Data Collection: Mining Valuable Customer Feedback
The first step involves scraping review data from LovBuy using Python libraries like BeautifulSoup or Scrapy. Key data points to collect include:
- Rating scores
- Review text content
- Purchase date
- Product specifications
- Images/videos
2. From Raw Data to Structured Spreadsheets
Using Python's Pandas library, we transform unstructured review data into structured spreadsheet columns:
| Product ID | Review Date | Rating | Keyword Tags | Sentiment Score |
|---|---|---|---|---|
| LB5421 | 2023-05-12 | 4.5 | quality,fast delivery | 0.82 |
Creating Actionable Insights Through Dashtars
The structured spreadserves two key purposes:#off your dashboard creation:
- Trend analysis:
< li>>>>Iss detection: