Nearshoring and Sustainable Economy News Scraper

Description

This project aims to develop a web scraper using Python, Scrapy, Selenium, and Beautiful Soup to gather news articles related to nearshoring and sustainable economy initiatives from various online sources. The scraped data will be stored in a structured format for further analysis and visualization.

Websites to Scrape.

• Forbes: https://www.forbes.com.mx/
• Proceso: https://www.proceso.com.mx/
• Expansión: https://expansion.mx/
• El Reforma: https://www.reforma.com/
• El Economista: https://www.eleconomista.com.mx/
• Bloomberg: https://www.bloomberg.com/
• El Financiero: https://www.elfinanciero.com.mx/
• Thomson Reuters: https://www.thomsonreutersmexico.com/es-mx
• KPGM: https://kpmg.com/mx/es/home.html
• Deloitte: https://www2.deloitte.com/mx/es.html
• IMCO: https://imco.org.mx/
• Concanaco: https://www.concanaco.com.mx/
• Secretaría de economía: https://www.gob.mx/se
• Banco de México: https://www.banxico.org.mx/

Relevant terms.

• Recolocación de la inversión en México.
• Relocalización de las inversiones hacia México.
• Inversión extranjera directa en México.
• Nearshoring
• Nearshoring en México
• Recolocación de las cadenas de mininistro.
• Inversión extranjera.

Technologies

• Python: The primary programming language for the project.
• Scrapy: A powerful web scraping framework for building efficient and scalable scrapers.
• Selenium: A browser automation tool for handling dynamic web pages and JavaScript-rendered content.
• Beautiful Soup: A Python library for parsing and extracting data from HTML and XML documents.

Project Setup

1. Clone the repository:

git clone https://github.com/SPQR14/discovery_web_scrapper.git

2. Install dependencies:

pip install -r requirements.txt

Scraping Process

1. Define target websites: Identify the list of websites from which news articles will be scraped.

2. Create Scrapy spiders: Develop Scrapy spiders for each target website, utilizing Selenium and Beautiful Soup to extract relevant news articles and their content.

3. Data storage: Implement a data storage mechanism to save the scraped data in a structured format, such as CSV or a database.

Running the Scraper

1. Execute Scrapy spiders: Run the Scrapy spiders using the Scrapy crawl command to initiate the scraping process.

2. Monitor and maintain: Regularly monitor the scraper's performance and update the spiders as needed to adapt to changes in the target websites.

Data Analysis and Visualization

1. Analyze scraped data: Perform data analysis on the scraped news articles to identify trends, patterns, and insights related to nearshoring and sustainable economy initiatives.

2. Create visualizations: Generate visualizations, such as charts and graphs, to present the findings from the data analysis in a clear and concise manner.

Project Contributions

Contributions to this project are welcome, including:

• Improving scraper efficiency: Optimizing the scraping process to enhance speed and resource utilization.
• Expanding data sources: Adding new target websites to broaden the scope of news coverage.
• Enhancing data analysis: Developing more sophisticated data analysis techniques to extract deeper insights.
• Creating interactive visualizations: Designing interactive visualizations to facilitate data exploration and understanding.

Future Directions

• Machine learning integration: Incorporate machine learning techniques to classify news articles, identify sentiment, and summarize key points.
• Real-time data streaming: Implement real-time data streaming to capture and process news articles as they are published.
• Data sharing and collaboration: Establish a platform for sharing and collaborating on scraped data and analysis findings with the research community.

This project provides a valuable tool for gathering and analyzing news information related to nearshoring and sustainable economy initiatives. By combining web scraping techniques with data analysis and visualization, the project can contribute to a better understanding of these critical topics.