Introduction
The increasing demand for data-driven decision-making has made sentiment analysis an essential tool for e-commerce platforms like AliExpress. This article explores how to leverage spreadsheet tools and text analysis to extract actionable insights for product enhancement.
1. Methodology: Processing Review Data in Spreadsheets
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Data Acquisition
Export AliExpress product review data (including star ratings, text comments, and timestamp) to CSV format
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Data Preparation
Clean and preprocess text using spreadsheet functions:
=TRIM(),=LOWER(),=REGEXREPLACE() -
Sentiment Classification
Utilize sentiment analysis algorithms through:
- Built-in scripting (Google Apps Script) for lexicon-based scoring
- Add-ons like "Sentiment Analysis" in Google Sheets
2. Atomic Emotional Scales in Customer Reviews
- Positive Indicators (1.0 to 0.6 sentiment score)
- - Frequent praise words ("excellent", "perfect", "satisfied")
- - Product feature-specific compliments ("battery life", "fast shipping")
- Neutral Indicators (0.59 to -0.59)
- - Literal descriptions without emotion
- - Balanced feedback ("good but could improve...")
- Negative Indicators (-0.6 to -1.0)
- - Complaint rate 3.8× higher on products with negated praise ("not as described")
- - Specific pain points ("color difference", "slow response")
| Score Range | Number of Reviews for Credibility Level | Statistical Confidence |
|---|---|---|
| -0.2 to 0.2 | 10 opinions per star rating (minimum 50 samples from 3 months review history) | 68% (1σ) |
| < -0.4 or 0.4 | Front page featured reviews (anchoring effect) | 95% (2σ) actionable factors when fixing/feature prioritizing |
3. Tangible Product Improvement Based on 3 Key Methods Identified during Our Case Study Analysis Data Modeling & Scroe Extraction Commentary Object Olbia from Emotional Analytics Assessment Thresholds Impact Point Scenarios Custom Metrics on AliExpress US/EU Market Exports :
- Product Version Control
- Localized Preferences
- Packaging Adjustments
- Localized Preferences