Flipkart Reviews Sentiment Analysis using Python

Sentiment analysis is a Natural Language Processing technique that determines whether text expresses positive, negative, or neutral sentiment. In this tutorial, we'll analyze Flipkart product reviews using Python to understand customer opinions and satisfaction levels.

Installation and Setup

First, install the required Python libraries for web scraping and sentiment analysis ?

pip install beautifulsoup4
pip install textblob
pip install requests

Import the necessary modules for the analysis ?

import requests
from bs4 import BeautifulSoup
from textblob import TextBlob

Algorithm Steps

The sentiment analysis process follows these key steps ?

• Extract Flipkart reviews using web scraping techniques

• Preprocess reviews by removing unwanted characters, numbers, and punctuations

• Perform sentiment analysis using TextBlob library

• Classify sentiment based on polarity scores

• Calculate overall sentiment by averaging individual ratings

Complete Implementation

Here's a complete example that scrapes Flipkart reviews and analyzes their sentiment ?

import requests
from bs4 import BeautifulSoup
from textblob import TextBlob

# Sample Flipkart product review URL
url = "https://www.flipkart.com/beston-37-keys-piano-keyboard-toy-microphone-usb-power-cable-sound-recording-function-analog-portable/product-reviews/itmfgdhqpccb9hqb?pid=MKDFGDJXZYND3PSZ&lid=LSTMKDFGDJXZYND3PSZKIDQV7&marketplace=FLIPKART&page=5"

# Send request to fetch the webpage
response = requests.get(url)

# Parse HTML content
soup = BeautifulSoup(response.text, 'html.parser')

# Find all review elements
reviews = soup.find_all('div', {'class': 't-ZTKy'})

# Analyze sentiment for first 5 reviews
for review in reviews[:5]:
    text = review.text
    
    # Clean the text
    text = ' '.join(text.split())  # Remove extra whitespace
    text = ''.join(e for e in text if e.isalnum() or e.isspace())  # Keep only alphanumeric and spaces
    
    # Calculate sentiment polarity
    sentiment_score = TextBlob(text).sentiment.polarity
    
    print(f"Review: {text}")
    print(f"Sentiment Score: {sentiment_score:.3f}")
    
    # Classify sentiment
    if sentiment_score > 0.2:
        print("Analysis: Positive")
    elif sentiment_score < -0.2:
        print("Analysis: Negative")
    else:
        print("Analysis: Neutral")
    
    print("=" * 40)

Sample Output

Review: Third class or lower class product I dont want to abuse but product and seller deserve it Product stopped working after 1 month useShameful for flipkart to keep such type cheap products Quality of flipkart is really degrade and selling products like street vendors Third class qualityREAD MORE
Sentiment Score: 0.183
Analysis: Negative

Review: Ok ok productREAD MORE
Sentiment Score: 0.500
Analysis: Positive

Review: Nice but price highREAD MORE
Sentiment Score: 0.550
Analysis: Positive

Review: My piano was problemREAD MORE
Sentiment Score: 0.500
Analysis: Positive

Review: Vary bad prodectREAD MORE
Sentiment Score: -0.100
Analysis: Negative

Understanding Sentiment Scores

TextBlob returns polarity scores that help classify sentiments ?

Business Applications

Sentiment analysis of Flipkart reviews provides valuable insights for businesses ?

• Customer satisfaction monitoring Track overall sentiment trends

• Product improvement Identify common complaints and issues

• Competitive analysis Compare sentiment across different products

• Market prediction Forecast demand based on customer sentiment

• Quality assurance Detect quality issues early through negative feedback

Enhanced Analysis Features

To scale this analysis, you can extend the implementation to ?

• Process multiple pages of reviews automatically

• Save results to CSV files or databases for further analysis

• Generate sentiment distribution charts and visualizations

• Implement keyword extraction to identify common themes

• Set up automated alerts for sudden sentiment changes

Conclusion

Sentiment analysis on Flipkart reviews using Python and TextBlob provides valuable insights into customer opinions. This technique helps businesses understand customer satisfaction, identify improvement areas, and make datadriven decisions to enhance their products and services.