https://github.com/geetisha/web-scraping-and-sentiment-analysis

Web Scraping and Sentiment Analysis
https://github.com/geetisha/web-scraping-and-sentiment-analysis

jupyter-notebook matplotlib nltk numpy pandas python seaborn selenium vader-sentiment-analysis

Last synced: 3 months ago
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Web Scraping and Sentiment Analysis

• Host: GitHub
• URL: https://github.com/geetisha/web-scraping-and-sentiment-analysis
• Owner: geetisha
• Created: 2025-03-09T09:20:38.000Z (4 months ago)
• Default Branch: main
• Last Pushed: 2025-03-18T08:48:09.000Z (3 months ago)
• Last Synced: 2025-03-18T09:38:04.210Z (3 months ago)
• Topics: jupyter-notebook, matplotlib, nltk, numpy, pandas, python, seaborn, selenium, vader-sentiment-analysis
• Language: Jupyter Notebook
• Homepage:
• Size: 35.2 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# **Web Scraping and Sentiment Analysis**

## **Project Overview**

The **Web Scraping and Sentiment Analysis** project involves extracting customer reviews from e-commerce websites using **Python and Selenium** and analyzing their sentiment to derive meaningful business insights. The project focuses on understanding customer opinions, categorizing reviews into positive, negative, and neutral sentiments, and visualizing trends that can help improve product offerings and customer satisfaction.

## **Objectives**

- **Web Scraping:** Extract product reviews from an e-commerce website using **Selenium**.

- **Data Cleaning & Preprocessing:** Handle missing values, remove duplicates, and clean text data.

- **Sentiment Analysis:** Use **Natural Language Processing (NLP)** techniques to classify reviews as **positive, negative, or neutral**.

- **Visualization & Insights:** Generate charts and graphs using **Matplotlib & Seaborn** to showcase sentiment trends.

- **Business Recommendations:** Provide insights to help businesses improve customer satisfaction and product quality.

## **Tools & Technologies Used**

- **Python** for scripting and data processing

- **Selenium** for web scraping

- **Pandas & NumPy** for data manipulation

- **NLTK or Vader** for sentiment analysis

- **Matplotlib & Seaborn** for data visualization

- **Jupyter Notebook** for interactive analysis

## **Key Analysis & Insights**

- **Customer Feedback Trends:** Identify the overall sentiment distribution among customer reviews.

- **Product Performance:** Analyze which products receive the most positive or negative feedback.

- **Customer Pain Points:** Identify common issues faced by customers based on negative reviews.

- **Market Strategy Optimization:** Use sentiment insights to improve marketing strategies, customer engagement, and product quality.

## **Results & Business Impact**

- Helps businesses **understand customer satisfaction levels** through sentiment trends.

- Assists in **product improvement** by identifying common complaints and suggestions.

- Supports **data-driven decision-making** by providing actionable insights for better marketing and customer service strategies.