extractnetvstrafilatura
ExtractNet is an automated web data extraction tool using machine learning to parse HTML and text data.
This tool can be used in web scraping to automatically extract details from scraped HTML documents. While it's not as accurate as structured extraction using HTML parsing tools like CSS selectors or XPath it can still parse a lot of details.
Trafilatura is a Python package and command-line tool designed to gather text on the Web. It includes discovery, extraction and text processing components. Its main applications are web crawling, downloads, scraping, and extraction of main texts, metadata and comments. It aims at staying handy and modular: no database is required, the output can be converted to various commonly used formats.
Going from raw HTML to essential parts can alleviate many problems related to text quality, first by avoiding the noise caused by recurring elements (headers, footers, links/blogroll etc.) and second by including information such as author and date in order to make sense of the data. The extractor tries to strike a balance between limiting noise (precision) and including all valid parts (recall). It also has to be robust and reasonably fast, it runs in production on millions of documents.
This tool can be useful for quantitative research in corpus linguistics, natural language processing, computational social science and beyond: it is relevant to anyone interested in data science, information extraction, text mining, and scraping-intensive use cases like search engine optimization, business analytics or information security.
Example Use
import requests
from extractnet import Extractor
raw_html = requests.get('https://currentsapi.services/en/blog/2019/03/27/python-microframework-benchmark/.html').text
results = Extractor().extract(raw_html)
{'phone_number': '555-555-5555', 'email': 'example@example.com'}
# it can be used to clean HTML files
from trafilatura import clean_html
html = '<html><head><title>My Title</title></head><body><p>This is some <b>bold</b> text.</p></body></html>'
cleaned_html = clean_html(html)
print(cleaned_html)
# can strip away tags:
clean_html(html, tags_to_remove=["title"])
# or attributes
clean_html(html, attributes_to_remove=["title"])