Introduction
The world of purchasing agents (Daigou) has exploded in recent years, with consumers around the world relying on these intermediaries to access Chinese products. With this growth comes an urgent need for reputation analysis systems to evaluate product quality and purchasing agent reliability.
The Data Pipeline
Our analysis system achieves this through a three-step process:
- Scraping CNfans reviews
- Processing natural language
- Structuring data into spreadsheets
- Processing natural language
NLP Techniques Applied
We employ several sentiment analysis techniques to extract meaning from reviews:
- VADER for emotional tone detection
- TF-IDF for keyword importance scoring
- Contextual embeddings for understanding nuanced feedback
# Sample sentiment scoring function
def analyze_review(text):
analyzer = SentimentIntensityAnalyzer()
return analyzer.polarity_scores(text)
Spreadsheet Structure
The final output is organized into five key columns:
| Column | Content | Example |
|---|---|---|
| Product ID | Standardized identifier | JDF-83295 |
| Rating | 1-5 Star score | ★★★★☆ |
| Summary | Short review highlight | "Reliable sizing but slow packaging" |
| Score | Percentage rating from NLP | 82.4% |
| Pros/Cons | Binary for spreadsheet filtering | CSV-compatible format |
Practical Applications
For Consumers
Purchasing decisions become data-driven with access to analyzed review aggregations in simple spreadsheet format that anyone can understand and sort.
For Agents
Professional Purchasers can track their performance across multiple products and identify improvement opportunities in their service based on customer feedback.
For Platforms
Marketplacess like Taobao can integrage this analysis into their buying interfaces to highlight trusted agents and quality products.
Development Challenges
Key obstacles in building the system included Chinese-to-English translation nuance loss, detecting sarcasm in translated content, and variance between e-commerce platform rating standards leveraging in asymptotic Analysis at scale.