This project is a web scraper built using Selenium and Python to extract customer reviews for a specific product from Flipkart. The scraper navigates through multiple pages of reviews, collects the data, and saves it into a CSV file.
The aim of this project is to leverage machine learning techniques to perform sentiment analysis on customer reviews from eCommerce platforms. By analyzing customer sentiment, the project seeks to extract valuable insights that can help eCommerce businesses understand customer satisfaction levels, identify trends, and improve their products and services.
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Sentiment Classification: Develop machine learning models that can classify customer reviews into positive, negative, or neutral sentiments. This classification helps in quantifying customer satisfaction and dissatisfaction levels.
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Feature Extraction: Extract meaningful features from customer reviews, such as keywords, sentiment indicators, and product attributes mentioned by customers. These features provide deeper insights into customer preferences and pain points.
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Data Preprocessing: Clean and preprocess raw customer review data to enhance the quality and relevance of input data for machine learning models. This includes text normalization, tokenization, and handling of stopwords and special characters.
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Model Training: Train machine learning models using labeled customer review data. Experiment with various algorithms such as Naive Bayes, Support Vector Machines (SVM), or deep learning models like Recurrent Neural Networks (RNNs) or Transformers to find the most effective model for sentiment analysis.
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Model Evaluation: Evaluate the performance of trained models using metrics such as accuracy, precision, recall, and F1-score. Fine-tune models based on evaluation results to optimize performance.
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Deployment: Deploy the best-performing sentiment analysis model as a scalable solution that can analyze real-time customer reviews from eCommerce platforms. Integration with existing eCommerce systems allows for continuous feedback analysis.
- Overview
- Insights
- Installation
- Usage
- Project Structure
- Explanation of the Code
- Conclusion
- Contributing
- License
- Overview The Flipkart Product Reviews Scraper is designed to automate the process of extracting customer reviews from Flipkart for a specific product. This tool is particularly useful for data analysts, researchers, and developers who want to perform sentiment analysis, gain insights into customer opinions, or simply collect data for personal use. The script navigates through multiple pages of reviews, extracting key information such as ratings, review titles, review content, reviewer names, locations, and other details, and compiles them into a structured CSV file for further analysis.
By using this scraper, users can gain valuable insights into:
- Sentiment: Analyze the overall sentiment of the reviews to understand how customers feel about the product.
- ommon Issues: Identify frequent complaints or issues raised by customers.
- Product Features: Understand which features customers appreciate the most and which ones they are dissatisfied with.
- Market Trends: Compare reviews across different time periods to identify trends in customer feedback.
- Customer Demographics: Gather information about the locations of customers to understand the geographic distribution of the product's user base.
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Clone the Repository:[git clone https://github.com/augustine-aj/flipkart-Product-Reviews-scraper.git cd flipkart-reviews-scraper
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Install Required Packages: Make sure you have pip installed. Then, run pip install selenium pandas
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Download ChromeDriver: Download the appropriate version of ChromeDriver for your Chrome browser from here. Extract the file and update the path variable in the script with the path to your ChromeDriver executable.
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Run the Script: python webscrape-flipkart.py
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Check the Output: The extracted data will be saved in review_data.csv in the project directory.
- webscrape-flipkart.py: Main script that contains the web scraping logic.
- review_data.csv: Output CSV file containing the extracted review data.
This project demonstrates how to use Selenium for web scraping tasks, specifically extracting product review data from Flipkart. The extracted data can be used for various analytical purposes, such as sentiment analysis, market research, and customer feedback analysis. By automating the data collection process, this scraper saves time and effort, allowing users to focus on analyzing the data and deriving actionable insights.
Live coding recorded video link:
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