Google Maps Review Sentiment Analysis

Sentiment Analysis Dashboard

Project Overview

This comprehensive sentiment analysis project combines web scraping and natural language processing to extract and analyze customer reviews from Google Maps. The system provides businesses with actionable insights into customer sentiment patterns, enabling data-driven improvements to products and services. The project demonstrates advanced capabilities in web scraping, data processing, and sentiment analysis using machine learning techniques.

Key Features

  • Automated Web Scraping: Custom Google Maps scraper with Selenium for dynamic content extraction
  • Multi-Sorting Capabilities: Reviews sorted by relevance, newest, highest rating, and lowest rating
  • Comprehensive Data Collection: Extracts review text, ratings, user information, dates, and metadata
  • Sentiment Analysis: Advanced NLP techniques to classify review sentiment and emotional tone
  • Batch Processing: Handles multiple business locations simultaneously for comparative analysis
  • Data Export: Structured CSV output for further analysis and visualization
  • Banking Industry Focus: Specialized analysis for financial services customer feedback

Technical Implementation

Web Scraping Architecture

The system employs sophisticated web scraping techniques to overcome dynamic content challenges:

  • Selenium WebDriver: Handles JavaScript-rendered content and dynamic loading
  • BeautifulSoup Integration: Efficient HTML parsing and data extraction
  • Anti-Detection Measures: Implements delays and user-agent rotation to avoid blocking
  • Error Handling: Robust exception handling for network issues and page structure changes

Data Processing Pipeline

Data Extraction

Automated scraping of Google Maps reviews with configurable parameters for review count, sorting methods, and target businesses.

Sentiment Analysis

Advanced NLP processing to classify sentiment polarity, emotional tone, and extract key themes from customer feedback.

Analytics Generation

Statistical analysis and visualization of sentiment trends, rating distributions, and temporal patterns in customer feedback.

Data Export

Structured data output in CSV format with comprehensive metadata for business intelligence and reporting applications.

Technology Stack

  • Web Scraping: Selenium WebDriver for dynamic content handling
  • HTML Parsing: BeautifulSoup4 for efficient data extraction
  • Data Processing: Pandas and NumPy for data manipulation and analysis
  • Database Integration: PyMongo for MongoDB data storage
  • HTTP Requests: Requests library for API interactions
  • Browser Automation: WebDriver Manager for automated browser setup
  • Development Tools: Jupyter Notebooks for iterative analysis and prototyping
  • Visualization: Python plotting libraries for data visualization

Business Applications

This project provides valuable insights for various business applications:

  • Customer Experience Management: Identify pain points and improvement opportunities from customer feedback
  • Competitive Analysis: Compare sentiment patterns across competing businesses in the same area
  • Brand Monitoring: Track sentiment trends over time to measure brand health
  • Service Quality Assessment: Quantify customer satisfaction and service quality metrics
  • Marketing Intelligence: Understand customer preferences and communication preferences
  • Operational Insights: Identify specific service areas requiring attention or investment

Banking Industry Focus

Special emphasis on financial services analysis:

  • State Bank of India (SBI) branch review analysis for service quality assessment
  • Customer satisfaction metrics specific to banking services
  • Identification of common banking service complaints and praise patterns
  • Branch-specific performance insights for operational improvements

Data Output Structure

The system generates comprehensive datasets including:

  • Review Metadata: Review ID, date, retrieval timestamp, source URL
  • Content Analysis: Review text, rating scores, sentiment classification
  • User Information: Username, review count, photo contributions
  • Temporal Data: Review posting dates and trending analysis
  • Geographical Context: Business location and regional sentiment patterns

Technical Challenges Solved

  • Dynamic Content Loading: Handling JavaScript-rendered reviews with proper wait conditions
  • Rate Limiting: Implementing respectful scraping practices to avoid service disruption
  • Data Quality: Cleaning and preprocessing review text for accurate sentiment analysis
  • Scalability: Batch processing capabilities for large-scale data collection
  • Error Recovery: Robust exception handling for network failures and page structure changes