From AcBuy Review to Spreadsheet: Building a Reputation Analysis System for Shopping Agents
With the rapid development of cross-border e-commerce, shopping agent services have gained significant popularity. Analyzing product reviews efficiently has become crucial for both agents and consumers. This article explores how to transform scattered review data into structured spreadsheets for reputation analysis.
The System Framework
Our reputation analysis system consists of three main components:
- Data Collection Module: Web crawlers extract review data from platforms like AcBuy
- Text Processing Engine: NLP techniques filter and analyze review content
- Visualization Interface: Dynamic dashboards display evaluation results
The core innovation lies in transforming unstructured text into spreadsheet-ready quantitative metrics.
Revolutionizing Review Processing
1. Crawler Configuration
Our Python-based crawler targets review sections with these parameters:
review.select('div.comment-text p').get_text(strip=True)
2. Sentiment Scoring
Using NLTK's VADER, we calculate polarity scores:
from nltk.sentiment import SentimentIntensityAnalyzer sia = SentimentIntensityAnalyzer() review_score = sia.polarity_scores(text)['compound']
Excel Power Query Magic
The processed data feeds into Excel through:
- CSV exports from Python
- Power Query transformations
- Pivot table summarization
| Product ID | Avg. Rating | Sentiment Score | Keyword Frequency |
|---|---|---|---|
| AC2024 | 4.8 | 0.72 | quality(38), fast(22) |
Delivering Actionable Insights
The system helps shopping agents:
Trend Identification
Track sentiment changes across product versions
Competitor Benchmarking
Compare review scores with alternative products
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The system helps shopping agents:
Trend Identification
Track sentiment changes across product versions
Competitor Benchmarking
Compare review scores with alternative products
Quality Alerting
Receive warnings when negative review trends emerge
"The spreadsheet reports helped us detect packaging issues 3 weeks before they became apparent in return rates"
Future Enhancements
Next-phase developments include:
- Multilingual review analysis
- Automatic review response generation
- Integration with inventory systems
This transformation from raw reviews to business intelligence exemplifies how simple spreadsheet systems can yield powerful analytical capabilities for shopping agents.