Introduction

In today's e-commerce landscape, customer reviews serve as invaluable feedback for product development. This article explores how to leverage AliExpress review data by importing it into spreadsheet tools (like Excel or Google Sheets) to perform sentiment analysis, identify key improvement areas, and ultimately enhance product quality and user experience.

Through text mining techniques and sentiment analysis algorithms, sellers can systematically process unstructured review data to gain actionable insights.

Methodology

1. Data Collection & Preparation

   Export product reviews from AliExpress and structure the data in spreadsheet columns:

   • Review text
   • Ratings (1-5 stars)
   • Date
   • Product variation (if applicable)

2. Sentiment Analysis Implementation

   Tools used: Built-in functions or add-ons like:

   • Google Sheets' JS scripting for custom sentiment analysis
   • Excel's Power Query with R/Python integration
   • Third-party plugins (e.g., MonkeyLearn, Lexalytics)

   Categorizes reviews as: Positive (⭐⭐⭐⭐⭐-⭐⭐⭐⭐), Neutral (⭐⭐⭐), Negative (⭐⭐-⭐)

3. Keyword Extraction

   Identify高频词汇through:

   • Word clouds generated from review corpus
   • TF-IDF (Term Frequency-Inverse Document Frequency) analysis
   • Manual tagging for nuanced themes

Key Findings from Sample Analysis

Sentiment (n=1000 reviews)	% Share	Common Keywords
Positive	68%	"fast shipping", "good quality", "as described"
Neutral	21%	"average", "okay", "expected"
Negative	11%	"broken item", "late delivery", "not working"

Product Improvement Opportunities