Sentiment Analysis of AliExpress Reviews in Spreadsheets for Product Improvement

2025-04-28

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This article explores how to leverage AliExpress customer reviews through spreadsheet-based sentiment analysis to derive actionable insights for product enhancement.

1. Data Import and Preparation

The first step involves exporting AliExpress review data (including star ratings, text comments, and timestamps) into spreadsheets:

Use ImportXML/ImportHTML for automated data collection
Structure data in columns: ProductID, ReviewText, Rating, Date
Clean data by removing emojis, brand names, and punctuation

// Sample formula for basic sentiment scoring
=ARRAYFORMULA(IF(LEN(A2:A),
IFERROR(
(COUNTIF(SPLIT(LOWER(A2:A), " "), "happy") 
- COUNTIF(SPLIT(LOWER(A2:A), " "), "defect"))
/LEN(A2:A)*10, 0),""))

Basic sentiment scoring formula for Google Sheets

2. Implementing Sentiment Analysis

Approach A: Lexicon-Based Analysis

Utilize sentiment lexicons (AFINN, VADER) through:

Building polarity dictionaries in adjacent sheets
Creating custom functions using Apps Script
Assigning sentiment scores: Negative (-5→1), Neutral (2→3), Positive (4→5)

Approach B: Machine Learning Integration

For advanced analysis, connect spreadsheets to ML APIs:

Google Cloud Natural Language API
Microsoft Azure Text Analytics
Custom Python sentiment models via Sheets ↔ Colab integration

3. Extracting Actionable Insights

Feature-Specific Sentiment

"Battery life" appears in 42% of negative reviews with average score 2.1

Comparative Analysis

Product A scores 8% higher on "ease of use" than competitor benchmark

Key tools for pattern identification:

Tool	Application
Word Trees	Visualize adjective-noun relationships
Pivot Tables	Compare sentiment by product version

4. Product Improvement Pathways

Material Quality
Prioritize upgrades to polymer composition for durability complaints regarding part X

Packaging

Address 22 recurring comments about inadequate protective materials

Fig 3. Sentiment trend correlation with product iterations Q1-Q3 2023

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