Structured Data Extraction and Data Mining with Bright Data & Google Gemini

This workflow combines web data scraping and large language models to achieve structured data extraction and deep analysis of web pages. Users can automatically retrieve and parse web content, extract themes, identify trends, and conduct sentiment analysis, generating easy-to-understand reports. It supports saving results as local files and provides real-time notifications via Webhook, making it suitable for various scenarios such as media monitoring, market research, and data processing, significantly improving the efficiency and accuracy of data analysis.

Tags

Structured DataSentiment Analysis

Workflow Name

Structured Data Extraction and Data Mining with Bright Data & Google Gemini

Key Features and Highlights

This workflow combines Bright Data’s Web Unlocker product with Google Gemini’s large language model to efficiently extract and deeply analyze structured data from web pages. Leveraging multiple AI technologies—including LLM chains, information extraction, and sentiment analysis—it automatically converts web content into structured textual data, distills key topics, identifies trends by geographic location and industry classification, and outputs precise, easy-to-understand analytical reports. Additionally, it supports saving results as local files and real-time notifications via Webhook, enabling flexible data distribution and subsequent processing.

Core Problems Addressed

  • Overcomes challenges in scraping and unlocking data from dynamic web pages to ensure high-quality raw content acquisition.
  • Automates the complex process of converting unstructured web content into structured textual data.
  • Utilizes AI models for automatic topic extraction and trend analysis, significantly reducing manual data organization and insight mining efforts.
  • Integrates sentiment analysis to add an emotional dimension to the data, enhancing the depth and practical value of the analysis.

Application Scenarios

  • Media Monitoring and Public Opinion Analysis: Automatically scrape news websites to extract trending topics and patterns.
  • Market Research and Competitive Analysis: Identify the latest developments across different regions and industries.
  • Data Science and Engineering: Build structured datasets to support downstream machine learning and reporting tasks.
  • Content Aggregation Platforms: Automatically consolidate and categorize textual information from multiple channels.

Main Workflow Steps

  1. Manually trigger the workflow start.
  2. Configure the target webpage URL and the corresponding Bright Data unlock zone.
  3. Use Bright Data API to request the target webpage data and obtain raw content in Markdown format.
  4. Employ the Google Gemini model to extract text from the Markdown content, removing formatting to get plain text data.
  5. Apply the information extraction module to distill topics and analyze trends, outputting structured topic models and trend data clustered by location and category.
  6. Combine with Google Gemini for sentiment analysis to generate emotional summaries.
  7. Push the analysis results to a specified URL via Webhook for real-time data delivery.
  8. Save the topic and trend data as local JSON files for offline viewing and further processing.

Involved Systems and Services

  • Bright Data (Web Unlocker product): Dynamic web page data scraping and unlocking.
  • Google Gemini (PaLM API): Large language model used for text extraction, topic analysis, and sentiment analysis.
  • Webhook Service (e.g., Webhook.site): For real-time pushing of structured analysis results.
  • Local File System: For storing JSON files of topic and trend analysis results.

Target Users and Value Proposition

  • Data Engineers and Data Scientists: Simplify data collection and preprocessing workflows to rapidly build structured datasets.
  • Market Analysts and Business Decision Makers: Obtain real-time industry trends and regional insights to support strategic planning.
  • Media and Content Operations Teams: Automate large-scale text content collection and classification to improve content management efficiency.
  • AI and Automation Enthusiasts: Demonstrate an exemplary integration of web scraping technology with AI models for intelligent data mining.

This workflow integrates cutting-edge data acquisition and AI text analysis technologies, providing users with a fully automated solution from web content extraction to structured insights, greatly enhancing data processing efficiency and analysis quality.

Recommend Templates

Google Analytics Template

The main function of this workflow is to automatically retrieve website traffic data from Google Analytics, analyzing page engagement, search performance, and country distribution over the past two weeks. By utilizing AI to intelligently interpret the data, it generates professional SEO optimization recommendations and saves the results to a Baserow database for easier management and tracking. This process simplifies data comparison and analysis, enhancing the efficiency and accuracy of SEO decision-making, making it highly suitable for website operators and digital marketing teams.

Google AnalyticsSEO Optimization

Convert URL HTML to Markdown and Extract Page Links

This workflow is designed to convert webpage HTML content into structured Markdown format and extract all links from the webpage. By utilizing the Firecrawl.dev API, it supports batch processing of URLs, automatically managing request rates to ensure stable and efficient content crawling and conversion. It is suitable for scenarios such as data analysis, content aggregation, and market research, helping users quickly acquire and process large amounts of webpage information, reducing manual operations and improving work efficiency.

Web ScrapingContent Conversion

Smart Factory Data Generator

The smart factory data generator periodically generates simulated operational data for factory machines, including machine ID, temperature, runtime, and timestamps, and sends it to a designated message queue via the AMQP protocol. This workflow effectively addresses the lack of real-time data sources in smart factory and industrial IoT environments, supporting developers and testers in system functionality validation, performance tuning, and data analysis without the need for real devices, thereby enhancing overall work efficiency.

Smart FactoryData Generation

HTTP_Request_Tool (Web Content Scraping and Simplified Processing Tool)

This workflow is a web content scraping and processing tool that can automatically retrieve web page content from specified URLs and convert it into Markdown format. It supports two scraping modes: complete and simplified. The simplified mode reduces links and images to prevent excessively long content from wasting computational resources. The built-in error handling mechanism intelligently responds to request exceptions, ensuring the stability and accuracy of the scraping process. It is suitable for various scenarios such as AI chatbots, data scraping, and content summarization.

Web ScrapingMarkdown Conversion

Trustpilot Customer Review Intelligent Analysis Workflow

This workflow aims to automate the scraping of customer reviews for specified companies on Trustpilot, utilizing a vector database for efficient management and analysis. It employs the K-means clustering algorithm to identify review themes and applies a large language model for in-depth summarization. The final analysis results are exported to Google Sheets for easy sharing and decision-making within the team. This process significantly enhances the efficiency of customer review data processing, helping businesses quickly identify key themes and sentiment trends that matter to customers, thereby optimizing customer experience and product strategies.

Customer ReviewsSmart Analytics

Automated Workflow for Sentiment Analysis and Storage of Twitter and Form Content

This workflow automates the scraping and sentiment analysis of Twitter and external form content. It regularly monitors the latest tweets related to "strapi" or "n8n.io" and filters out unnecessary information. Using natural language processing technology, it intelligently assesses the sentiment of the text and automatically stores positively rated content in the Strapi content management system, enhancing data integration efficiency. It is suitable for brand reputation monitoring, market research, and customer relationship management, providing data support and high-quality content for decision-making.

Sentiment AnalysisAutomation Collection

Intelligent E-commerce Product Information Collection and Structured Processing Workflow

This workflow automates the collection and structured processing of e-commerce product information. By scraping the HTML content of specified web pages, it intelligently extracts key information such as product names, descriptions, ratings, number of reviews, and prices using an AI model. The data is then cleaned and structured, with the final results stored in Google Sheets. This process significantly enhances the efficiency and accuracy of data collection, making it suitable for market research, e-commerce operations, and data analysis scenarios.

E-commerce CollectionIntelligent Structuring

My workflow 2

This workflow automatically fetches popular keywords and related information from Google Trends in the Italian region, filters out new trending keywords, and uses the jina.ai API to obtain relevant webpage content to generate summaries. Finally, the data is stored in Google Sheets as an editorial planning database. Through this process, users can efficiently monitor market dynamics, avoid missing important information, and enhance the accuracy and efficiency of keyword monitoring, making it suitable for content marketing, SEO optimization, and market analysis scenarios.

Keyword MonitoringAutomated Crawling