Sentiment Analysis of AliExpress Product Reviews in Spreadsheets for Product Improvement-OK Spreadsheet-The best and new spreadsheet

Introduction

With the rapid growth of e-commerce platforms like AliExpress, customer reviews have become a goldmine of insights for sellers. This article explores how to harness AliExpress product review data using Google Sheets or Excel, combined with text analysis tools and sentiment analysis algorithms, to identify customer satisfaction points and pain points. The findings can guide product development and improvements, ultimately enhancing product quality and user experience.

Data Collection and Preparation

The first step involves extracting product review data from AliExpress. This can be achieved using web scraping tools, AliExpress API (if available), or by manually downloading available review reports. The collected data should include:

Review text
Star ratings
Dates of reviews
Product variants (if applicable)

Once collected, this data can be transferred to a Spreadsheet (Google Sheets or Microsoft Excel) for further analysis.

Conducting Sentiment Analysis

In Spreadsheets, sentiment analysis can be performed using various approaches:

Built-in tools or Add-ons:
Custom Scripts:
Formula-Based Analysis:

These tools assess each review's sentiment as positive, neutral, or negative, proportionally scoring sentiment intensity.

Keyword Extraction and Theme Identification

Beyond sentiment, analyzing the actual words customers use helps uncover consistent themes or recurring issues. Spreadsheet tools for identifying high-frequency keywords include:

1. Word Clouds or Frequency Analysis:

Top frequent words reveal what customers most frequently mention. Stop words (like “the,” “and”) should be excluded.

2. Bigrams and Trigrams:

Joining frequently co-occurring words (e.g., “battery life” or “shipping delay”) provides more context.

3. Categorization:

Create categories (e.g., "packaging," "performance," "service") and tag reviews accordingly.

Visualization charts (like bar graphs) can help identify the most common discussion topics related to satisfaction or frustration.

Deriving Insights for Product Improvement

Once reviews are classified by sentiment and categorized by theme, actionable insights can be extracted:

Comparing Sentiment Across Different Product Attributes:

Sellers with multiple product variants (e.g., colors or sizes) can segment sentiment analysis by attributes.
Identifying Mutual Chokepoints:

A high emergence of keywords like "slow shipping" suggests logistics optimization needs.

Linking Sentiment to Ratings

Cross-referencing negative sentiment scores with poorly rated reviews (e.g., 1-3 stars) shows progression roots.

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