Unlock Consumer Sentiment Via E-commerce Review Scraping

By Author: DataZivot
Total Articles: 74
Comment this article

Unlocking Consumer Sentiment with E-Commerce Review Scraping

Business Challenge

With millions of reviews posted daily on e-commerce platforms like Amazon, Flipkart, and Myntra, valuable consumer sentiment is more accessible than ever—with the right E-Commerce Reviews Scraping strategy to know exactly where and how to extract it.

A consumer electronics brand approached Datazivot to understand:

Why their top-rated product was seeing a drop in conversions.
What users were actually saying post-purchase.
How reviews compared across platforms like Amazon India, Flipkart, and Myntra (for their fashion line).
How to identify early warning signals for defects or product dissatisfaction.

Objectives

Datazivot was tasked with:

Scraping structured and unstructured Review data from Amazon, Flipkart, and Myntra.
Running sentiment analysis (positive/negative/neutral).
Detecting trending keywords and consumer concerns.
Benchmarking sentiment trends over time and across platforms.

Our Approach

1. Data Collection Using Smart Scraping
Using ...
... custom-built scraping tools, Datazivot extracted:

Product Reviews
Star Ratings
Review Titles & Descriptions
Review Dates
Reviewer Locations (if available)
Platforms:

Amazon India – Electronics & Home Appliances
Flipkart – Smartphones & Wearables
Myntra – Fashion (Men’s Shoes, Women’s Kurtis)
Volume: Over 85,000 reviews were collected over a 6-month period.

2. Natural Language Processing & Sentiment Analysis
Datazivot applied NLP models to classify each review:

Positive
Negative
Neutral
We used VADER and fine-tuned BERT models for sentiment scoring and aspect-based sentiment detection (e.g., battery life, delivery time, size fit).

3. Trend Analysis & Visualization
We built interactive dashboards to visualize:

Daily/weekly sentiment shifts
Trending complaints and compliments
Keyword clouds for product feature mentions (e.g., “battery,” “design,” “quality,” “return,” “fit”)
Sample Data Snapshot

Results & Business Impact

1. Product Team Adjustments
Found that 35% of negative reviews on Flipkart mentioned “charging issues.” The product team launched a hardware revision within 30 days.
2. Improved Inventory & Sizing Strategy
Myntra reviews flagged frequent size mismatches in one shoe brand. Datazivot’s insights helped the brand launch a more accurate size guide and reduce return rates by 18%.

3. Marketing & Messaging Optimization
Amazon reviews showed users appreciated packaging and delivery speed. The marketing team doubled down on this in their next campaign.

4. Platform-Specific Strategy
Flipkart buyers cared more about pricing and warranty, while Myntra customers emphasized fashion fit and design. This insight helped refine copy and platform strategy.

Visual Insights

Sentiment Breakdown (Product X on Amazon – Last 60 Days)
Positive: 62%
Neutral: 20%
Negative: 18%
Top Negative Keywords:
battery, charging, delay, return, poor fit

Top Positive Keywords:
delivery, fit, design, value, packaging

Tools & Technologies Used

Python (Scrapy, BeautifulSoup, Requests)
AWS Lambda for automation
Google Cloud NLP + HuggingFace BERT for sentiment tagging
Power BI for reporting dashboards

Conclusion
With the power of Review scraping, Datazivot enabled the client to move from reactive to proactive customer engagement. Insights from real user voices helped redesign products, improve campaigns, and streamline customer satisfaction across platforms.

The combination of review data, sentiment intelligence, and trend analysis gave the brand a strategic edge in an increasingly competitive e-commerce landscape.

Originally Published by https://www.datazivot.com/consumer-sentiment-ecommerce-review-data-scraping.php