newspaper-scraper 0.2.1

The all-in-one Python package for seamless newspaper article indexing, scraping, and processing – supports public and premium content!

Newspaper-Scraper

The all-in-one Python package for seamless newspaper article indexing, scraping, and processing – supports public and premium content!

Intro

While tools like newspaper3k and goose3 can be used for extracting articles from news websites, they need a dedicated article url for older articles and do not support paywall content. This package aims to solve these issues by providing a unified interface for indexing, extracting and processing articles from newspapers.

1. Indexing: Index articles from a newspaper website using the beautifulsoup package for public articles and selenium for paywall content.
2. Extraction: Extract article content using the goose3 package.
3. Processing: Process articles for nlp features using the spaCy package.

The indexing functionality is based on a dedicated file for each newspaper. A few newspapers are already supported, but it is easy to add new ones.

Supported Newspapers

Logo	Newspaper	Country	Time span	Number of articles
	Der Spiegel	Germany	Since 2000	tbd
	Die Welt	Germany	Since 2000	tbd
	Bild	Germany	Since 2006	tbd
	Die Zeit	Germany	Since 1946	tbd
	Handelsblatt	Germany	Since 2003	tbd
	Der Tagesspiegel	Germany	Since 2000	tbd
	Süddeutsche Zeitung	Germany	Since 2001	tbd

Setup

It is recommended to install the package in an dedicated Python environment.
To install the package via pip, run the following command:

pip install newspaper-scraper

To also include the nlp extraction functionality (via spaCy), run the following command:

pip install newspaper-scraper[nlp]

Usage

To index, extract and process all public and premium articles from Der Spiegel, published in August 2021, run the following code:

import newspaper_scraper as nps  
from credentials import username, password  
  
with nps.Spiegel(db_file='articles.db') as news:
    news.index_articles_by_date_range('2021-08-01', '2021-08-31')  
    news.scrape_public_articles()
    news.scrape_premium_articles(username=username, password=password)  
    news.nlp()

This will create a sqlite database file called articles.db in the current working directory. The database contains the following tables:

• tblArticlesIndexed: Contains all indexed articles with their scraping/ processing status and whether they are public or premium content.
• tblArticlesScraped: Contains metadata for all parsed articles, provided by goose3.
• tblArticlesProcessed: Contains nlp features of the cleaned article text, provided by spaCy.