FlipKart review analysis

Import necessary libraries for web scraping

import requests # For sending HTTP requests from bs4 import BeautifulSoup # For parsing HTML import pandas as pd # For data manipulation and storage

Define the Flipkart product URL to scrape

product_url = "https://www.flipkart.com/product-name/reviews"

Send an HTTP GET request to retrieve the web page

response = requests.get(product_url)

Check if the request was successful

if response.status_code == 200: # Parse the HTML content of the page using BeautifulSoup soup = BeautifulSoup(response.text, 'html.parser')

# Locate the review container element
review_container = soup.find('div', class_='review-container')

Loop through each review

for review in review_container.find_all('div', class_='review'):
    # Extract review details such as reviewer name, rating, date, and text
    reviewer_name = review.find('div', class_='reviewer-name').text
    rating = review.find('div', class_='rating').text
    review_date = review.find('div', class_='review-date').text
    review_text = review.find('div', class_='review-text').text

    # Store the extracted data in a dictionary or data frame

else: # Handle the case when the request fails print("Failed to retrieve the web page. Status code:", response.status_code)

Data preprocessing steps

- Clean and preprocess the review text

- Tokenize the text

- Perform text normalization (lowercasing, stemming, etc.)

- Remove stop words

Sentiment analysis using a pre-trained model

Data preprocessing steps

- Clean and preprocess the review text

- Tokenize the text

- Perform text normalization (lowercasing, stemming, etc.)

- Remove stop words

Sentiment analysis using a pre-trained model

- Assign sentiment scores to each review (positive, negative, neutral)

Data analysis and visualization

- Calculate summary statistics (average rating, sentiment distribution, etc.)

- Generate visualizations to represent the analysis results

Interpretation and insights

- Provide recommendations or conclusions based on the analysis

Save the analysis results to a file or database for reporting and future reference