The code examples in this repository were designed using Newspaper version: 0.2.8. The examples might require modification when there is a version update for Newspaper.
The last update to this repository was performed on 01-21-2021. All the examples worked based on the website structure of the news sources being queried at that time. If any news source modifies their website's navigational structure then the code example for that source might not function correctly.
For instance, the Die Zeit news site recently added an advertisement and tracking acknowledgement button, which will likely require the use of the Python library selenium coupled with Newspaper extraction code within this repository to extract article elements on that website.
It's worth pointing out that Newspaper has some extraction limitations, but most of these can be overcome with either snippets of addtional code or by including another Python library in the mix.
For example, the web page for Fox Baltimore cannot currently be parsed using either newspaper.build or newspaper Source. This is because the Fox Baltimore's page is rendered in JavaScript. To parse this page, one would need to use the Python module BeautifulSoup to extract the content, which can be further processed with newspaper.
I will update this repository as needed based on extraction questions that I find on either Stack Overflow or from Newspaper's issue tracker on GitHub.
This repository was developed to provide technical insights on how to properly utilized the Python library Newspaper3k to query news sources, such as the Wall Street Journal, the BBC and CNN.
Newspaper3k uses the Python requests module to make a connection request to a news website. Python requests allows connections to have HTTP headers information and Newspaper3k includes this capability within its code base. These Newspaper3k configuration parameters include: sending a browser's user agent string as part of the request, establishing a connection timeout period (in seconds) and using proxies.
Some websites queried with Newspaper3k will send back status response code indicating that there was a problem with the connection. These status response codes include:
- HTTP 400 Bad Request error
- HTTP 403 Forbidden client error
- HTTP 406 Not Acceptable client error
One of the primary root causes of these errors is the lack of a browser's user agent string in the request.
Another potential issue when making requests with Newspaper3k is a ReadTimeout error. This error is usually linked to not providing a connection timeout period in the request. The Python requests documentation makes a point that setting a connection timeout is considered best practice.
from newspaper import Config
config = Config()
config.browser_user_agent = string value
config.proxies = dictionary of proxies
config.request_timeout = int value from newspaper import Config
USER_AGENT = 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10.15; rv:78.0) Gecko/20100101 Firefox/78.0'
# add your proxy information
PROXIES = {
'http': "http://ip_address:port_number",
'https': "https://ip_address:port_number"
}
config = Config()
config.browser_user_agent = USER_AGENT
config.proxies = PROXIES
config.request_timeout = 10from newspaper import Config
from newspaper import Article
USER_AGENT = 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10.15; rv:78.0) Gecko/20100101 Firefox/78.0'
config = Config()
config.browser_user_agent = USER_AGENT
config.request_timeout = 10
base_url = 'https://www.wsj.com'
article = Article(base_url, config=config)
<DO SOMETHING>Newspaper3k also supports the use of HTTP headers via Config(). The headers are passed as a dictionary.
This example was written in response to this Newspaper issue: "How to use headers when requesting in Article()func?", which was posted on 09-16-2020.
from newspaper import Config
from newspaper import Article
HEADERS = {'user-agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10.15; rv:78.0) Gecko/20100101 Firefox/78.0',
'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8'}
config = Config()
config.headers = HEADERS
config.request_timeout = 10
base_url = 'https://www.wsj.com'
article = Article(base_url, config=config)
<DO SOMETHING>One of the primary purposes of Newspaper3k is text extraction from a news website. Out-of-box Newspaper3k does a good job of extracting content, but it is not flawless. Several of these extraction issues are posted as questions to either Stack Overflow or to the GitHub repository for Newspaper. Many of the extraction questions are directly related to an end-user not reviewing the news source's HTML code prior to querying the website with Newspaper3k. Any developer that has used BeautifulSoup, Scrapy or Selenium to scrape a website knows that you need to review the portal's structure to properly extract content.
BBC News stores their data elements in multiple locations within its source code. Some of these data elements can be extracted using article.meta_data and others can be accessed through the Python modules BeautifulSoup and JSON. As previously started BeautifulSoup is a dependency of Newspaper3k and can be accessed through newspaper.utils.
This example was written in response to this Newspaper issue: "Unable to pick up BBC Dates", which was posted on 07-11-2020.
import json
from newspaper import Config
from newspaper import Article
from newspaper.utils import BeautifulSoup
USER_AGENT = 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10.15; rv:78.0) Gecko/20100101 Firefox/78.0'
config = Config()
config.browser_user_agent = USER_AGENT
config.request_timeout = 10
base_url = 'https://www.bbc.com/news/health-54500673'
article = Article(base_url, config=config)
article.download()
article.parse()
print(article.title)
['Covid virus ‘survives for 28 days’ in lab conditions']
article_meta_data = article.meta_data
article_summary = {value for (key, value) in article_meta_data.items() if key == 'description'}
print(article_summary)
{'Researchers find SARS-Cov-2 survives for longer than thought - but only under certain conditions.'}
soup = BeautifulSoup(article.html, 'html.parser')
bbc_dictionary = json.loads("".join(soup.find("script", {"type":"application/ld+json"}).contents))
date_published = [value for (key, value) in bbc_dictionary.items() if key == 'datePublished']
print(date_published)
['2020-10-11T20:11:33.000Z']
article_author = [value['name'] for (key, value) in bbc_dictionary.items() if key == 'author']
print(article_author)
['BBC News']
# another method to extract the title
article_title = [value for (key, value) in bbc_dictionary.items() if key == 'headline']
print(article_title)
['Covid virus ‘survives for 28 days’ in lab conditions']The example below is querying an article on the CNN website using Newspaper3k. The article data elements; title, authors and date published are adequately extracted using Newspaper3k. The keywords for this article were not initial discovered by Newspaper3k, but modifying the parameter article.keywords to meta_keywords does yield the keywords related to this article.
This example was written in response to this Stack Overflow question: "Python: See timestamp of article provided by newspaper3k?", which was posted on 09-18-2020.
from newspaper import Config
from newspaper import Article
USER_AGENT = 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10.15; rv:78.0) Gecko/20100101 Firefox/78.0'
config = Config()
config.browser_user_agent = USER_AGENT
config.request_timeout = 10
url = 'https://www.cnn.com/2020/10/09/business/edinburgh-woollen-mill-job-cuts/index.html'
article = Article(url, config=config)
article.download()
article.parse()
print(article.title)
Another 24,000 retail jobs at risk as UK fashion group faces collapse
print(article.publish_date)
2020-10-09 00:00:00
print(article.authors)
['Hanna Ziady', 'Cnn Business']
print(article.keywords)
[] returned an empty list
print(article.meta_keywords)
['business', 'Edinburgh Woollen Mill: 24', '000 jobs at risk as company appoints administrators - CNN']Extracting specific data elements from Fox News requires querying the meta tags section of the HTML code. The data elements that can be extracted include the title of the article, the published date of the article and a summary of the article. Fox News does not use keywords, so extracting these is not possble. Extracting the authors of the article is also problematic, because Fox News does not use a standard tag (e.g., by-line) for this information.
from newspaper import Config
from newspaper import Article
USER_AGENT = 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10.15; rv:78.0) Gecko/20100101 Firefox/78.0'
config = Config()
config.browser_user_agent = USER_AGENT
config.request_timeout = 10
base_url = 'https://www.foxbusiness.com/economy/white-house-calls-for-interim-coronavirus-relief-as-negotiations-continue'
article = Article(url, config=config)
article.download()
article.parse()
article_meta_data = article.meta_data
article_title = {value for (key, value) in article_meta_data.items() if key == 'dc.title'}
print(article_title)
{'White House pushes for limited coronavirus relief bill as broader effort meets resistance'}
article_published_date = str({value for (key, value) in article_meta_data.items() if key == 'dcterms.created'})
print(article_published_date)
{'2020-10-11T12:51:53-04:00'}
article_summary = {value for (key, value) in article_meta_data.items() if key == 'dc.description'}
print(article_summary)
{'In the letter to House and Senate members, Mnuchin and Meadows said the White House would continue to talk to Senate Democratic Leader Chuck Schumer
and House Speaker Nancy Pelosi, but that Congress should "immediately vote on a bill" that would enable the use of unused Paycheck Protection Program
funds while working toward a bigger package.'}Fox News stores the data elements article title, article summary, article author and date published in a script tag. These elements can be extracted using the Python modules BeautifulSoup and JSON. BeautifulSoup is a dependency of Newspaper3k and can be accessed through newspaper.utils.
import json
from newspaper import Config
from newspaper import Article
from newspaper.utils import BeautifulSoup
USER_AGENT = 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10.15; rv:78.0) Gecko/20100101 Firefox/78.0'
config = Config()
config.browser_user_agent = USER_AGENT
config.request_timeout = 10
base_url = 'https://www.foxbusiness.com/economy/white-house-calls-for-interim-coronavirus-relief-as-negotiations-continue'
article = Article(base_url, config=config)
article.download()
article.parse()
soup = BeautifulSoup(article.html, 'html.parser')
cnn_dictionary = json.loads("".join(soup.find("script", {"type":"application/ld+json"}).contents))
date_published = [value for (key, value) in cnn_dictionary.items() if key == 'datePublished']
print(date_published)
['2020-10-11T12:51:53-04:00']
article_author = [value['name'] for (key, value) in cnn_dictionary.items() if key == 'author']
print(article_author)
['Reuters']
article_title = [value for (key, value) in cnn_dictionary.items() if key == 'headline']
print(article_title)
['White House pushes for limited coronavirus relief bill as broader effort meets resistance']
article_summary = [value for (key, value) in cnn_dictionary.items() if key == 'description']
print(article_summary)
['In the letter to House and Senate members, Mnuchin and Meadows said the White House would continue to talk to Senate Democratic Leader Chuck Schumer
and House Speaker Nancy Pelosi, but that Congress should "immediately vote on a bill" that would enable the use of unused Paycheck Protection Program
funds while working toward a bigger package.']Extracting data elements from the website Fox Baltimore with either Newspaper Build or Newspaper Source is currently not possible. Fox Baltimore embeds the bulk of its content in script tags. This data can be extracted using the Python modules BeautifulSoup and JSON. BeautifulSoup is a dependency of Newspaper3k and can be accessed through newspaper.utils.
As of 11-18-2020 the example below can extract content like Newspaper Build or Newspaper Source does from the main page of a news source.
This example was written in response to this Newspaper issue: "Newspaper not extracting pages for Fox Baltimore", which was posted on 11-14-2020.
import json
import requests
import pandas as pd
from newspaper import Config
from newspaper import Article
from newspaper.utils import BeautifulSoup
HEADERS = {'user-agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10.15; rv:78.0) Gecko/20100101 Firefox/78.0',
'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8'}
def query_foxbaltimore_news():
df_foxbaltimore_extraction = pd.DataFrame(columns=['article_category', 'date_published', 'article authors',
'article title', 'article summary', 'article keywords',
'article url', 'article text'])
url = 'http://foxbaltimore.com/'
response = requests.get(url, headers=HEADERS, allow_redirects=True, verify=True, timeout=30)
soup = BeautifulSoup(response.content, 'html.parser')
fox_soup = soup.find_all("script", {"type": "application/json"})[1]
fox_json = json.loads(''.join(fox_soup))
for news in fox_json['content']['page-data']['teaser']:
for article in news['teasers']:
article_category = article['categories'][0]
article_title = article['title']
article_url = f"https://foxbaltimore.com{article['url']}"
article_summary = article['summary']
article_published_date = article['publishedDateISO8601']
if 'sponsored' not in article_url:
article_details = query_individual_article_elements(article_url)
df_foxbaltimore_extraction = df_foxbaltimore_extraction.append({'article category':article_category,
'date_published': article_published_date,
'article authors': article_details[0],
'article title': article_title,
'article summary': article_summary,
'article keywords': article_details[3],
'article url': article_url,
'article text': article_details[5]}, ignore_index=True)
return df_foxbaltimore_extraction
def query_individual_article_elements(url):
config = Config()
config.headers = HEADERS
config.request_timeout = 30
article = Article(url, config=config, memoize_articles=False)
article.download()
article.parse()
article_meta_data = article.meta_data
article_author = article.authors
article_published_date = str({value['published_time'] for (key, value) in article_meta_data.items()
if key == 'article'})
article_keywords = sorted([value.lower() for (key, value) in article_meta_data.items() if key == 'keywords'])
article_title = str({value for (key, value) in article_meta_data.items() if key == 'title'})
article_summary = {value for (key, value) in article_meta_data.items() if key == 'description'}
soup = BeautifulSoup(article.html, 'html.parser')
fox_soup = soup.find_all("script", {"type": "application/json"})[1]
fox_json = json.loads(''.join(fox_soup))
article_text = ''.join(fox_json['content']['main_content']['story']['richText'])
article_details = [article_author,
article_published_date,
article_title,
article_keywords,
article_summary,
article_text]
return article_detailsThe example below is querying an article on the Wall Street Journal and extracting several data elements from the page's HTML code. Newspaper3k was able to adequately extract the article's title and author of the article, but failed to extract the published date or the keywords related to this article.
from newspaper import Config
from newspaper import Article
USER_AGENT = 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10.15; rv:78.0) Gecko/20100101 Firefox/78.0'
config = Config()
config.browser_user_agent = USER_AGENT
config.request_timeout = 10
base_url = 'https://www.wsj.com/articles/investors-are-betting-corporate-earnings-have-turned-a-corner-11602408600?mod=hp_lead_pos1'
article = Article(base_url, config=config)
article.download()
article.parse()
print(article.title)
Investors Are Betting Corporate Earnings Have Turned a Corner
print(article.authors)
['Karen Langley']
print(article.publish_date)
None
print(article.keywords)
[] returned an empty listThe published date and keywords related to this Wall Street Journal article are located in mutiple meta tags and can be extracted by Newspaper3k using article.meta_data. Addtional article data elements, such as authors, title and article summary are also located within the meta tags section used by the Wall Street Journal.
from newspaper import Config
from newspaper import Article
USER_AGENT = 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10.15; rv:78.0) Gecko/20100101 Firefox/78.0'
config = Config()
config.browser_user_agent = USER_AGENT
config.request_timeout = 10
base_url = 'https://www.wsj.com/articles/investors-are-betting-corporate-earnings-have-turned-a-corner-11602408600?mod=hp_lead_pos1'
article = Article(base_url, config=config)
article.download()
article.parse()
article_meta_data = article.meta_data
article_published_date = str({value for (key, value) in article_meta_data.items() if key == 'article.published'})
print(article_published_date)
{'2020-10-11T09:30:00.000Z'}
article_author = sorted({value for (key, value) in article_meta_data.items()if key == 'author'})
print(article_author)
['Karen Langley']
article_title = {value for (key, value) in article_meta_data.items() if key == 'article.headline'}
print(article_title)
{'Investors Are Betting Corporate Earnings Have Turned a Corner'}
article_summary = {value for (key, value) in article_meta_data.items() if key == 'article.summary'}
print(article_summary)
{'Investors are entering third-quarter earnings season with brighter expectations for corporate profits,
a bet they hope will propel the next leg of the stock market’s rally.'}
keywords = ''.join({value for (key, value) in article_meta_data.items() if key == 'news_keywords'})
article_keywords = sorted(keywords.lower().split(','))
print(article_keywords)
['c&e exclusion filter', 'c&e industry news filter', 'codes_reviewed', 'commodity/financial market news', 'content types',
'corporate/industrial news', 'earnings', 'equity markets', 'factiva filters', 'financial performance']An unanswered Stack Overflow question from 2017 prompted me to explore how to extract article content from the Wayback Machine archives.
That question was attempting to use newspaper.build to extract the archived articles. I could not get newspaper.build to work correctly, but I was able to use newspaper Source to query and extract articles from the archives.
from time import sleep
from random import randint
from newspaper import Config
from newspaper import Source
USER_AGENT = 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10.15; rv:78.0) Gecko/20100101 Firefox/78.0'
config = Config()
config.browser_user_agent = USER_AGENT
config.request_timeout = 10
cnbc_wayback_archive = Source(url='https://web.archive.org/web/20180301012621/https://www.cnbc.com/', config=config,
memoize_articles=False, language='en', number_threads=20, thread_timeout_seconds=2)
cnbc_wayback_archive.build()
for article in cnbc_wayback_archive.articles:
article.download()
article.parse()
article_meta_data = article.meta_data
print(article.publish_date)
print(article.title)
article_description = "".join({value for (key, value) in article_meta_data.items() if key == 'description'})
print(article_description)
article_keywords = {value for (key, value) in article_meta_data.items() if key == 'keywords'}
print(list(article_keywords))
print(article.url)
# this sleep timer is helping with some timeout issues
# that happened when querying
sleep(randint(1, 5))Newspaper3k can be used to post-process HTML files that have stored offline. The example below downloads the HTML for a news article from CNN. After the article is downloaded the file is read into Newspaper and the data elements with the article are extracted.
This example was written in response to this Stack Overflow question: "how to extract from stored HTML using Python Newspaper", which was posted on 04-17-2017.
from newspaper import Config
from newspaper import Article
USER_AGENT = 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10.15; rv:78.0) Gecko/20100101 Firefox/78.0'
config = Config()
config.browser_user_agent = USER_AGENT
config.request_timeout = 10
base_url = 'https://www.cnn.com/2020/10/12/health/johnson-coronavirus-vaccine-pause-bn/index.html'
article = Article(url, config=config)
article.download()
article.parse()
with open('cnn.html', 'w') as fileout:
fileout.write(article.html)
# Read the HTML file created above
with open("cnn.html", 'r') as f:
# note the empty URL string
article = Article('', language='en')
article.download(input_html=f.read())
article.parse()
print(article.title)
Johnson & Johnson pauses Covid-19 vaccine trial after 'unexplained illness'
article_meta_data = article.meta_data
article_published_date = {value for (key, value) in article_meta_data.items() if key == 'pubdate'}
print(article_published_date)
{'2020-10-13T01:31:25Z'}
article_author = {value for (key, value) in article_meta_data.items() if key == 'author'}
print(article_author)
{'Maggie Fox, CNN'}
article_summary = {value for (key, value) in article_meta_data.items() if key == 'description'}
print(article_summary)
{'Johnson&Johnson said its Janssen arm had paused its coronavirus vaccine trial after an "unexplained illness" in one
of the volunteers testing its experimental Covid-19 shot.'}
article_keywords = {value for (key, value) in article_meta_data.items() if key == 'keywords'}
print(article_keywords)
{"health, Johnson & Johnson pauses Covid-19 vaccine trial after 'unexplained illness' - CNN"}Newspaper3k is designed to caches all previously extracted articles from a specific source. The primary reason for caching these articles is prevent duplicate querying for a given article. Newspaper3k has a parameter named memoize_articles, which is enabled to "True" by default.
For instance both of these queries have the parameter memoize_articles=True automatically set by Newspaper3k.
cnn_articles = newspaper.build('https://www.cnn.com/', config=config)
article = Article('https://www.cnn.com/2020/12/05/health/us-hospitals-covid-pandemic/index.html', config=config)With this parameter set to "True" newspaper will write information related to these queries to a temporary directory named .newspaper_scraper. This directory will have a minimum of two sub-directories, which are: feed_category_cache and memoized. The URLs for news sources will be written to a text file (e.g., www.cnn.com.txt) in the sub-directory memoized. The source code for Newspaper3k indicates that this cache will be maintained for 5 days. The cache is automatically updated with each query of given source (e.g. cnn.com).
I noted that even if you set the parameter memoize_articles to "False" these sub-directories are still created and one file is written to sub-directory feed_category_cache when using newspaper.build. So far, I have not found a method to prevent Newspaper3k from creating these sub-directories or redirecting them to a RAMDISK in memory.
cnn_articles = newspaper.build('https://www.cnn.com/', config=config, memoize_articles=False)
article = Article('https://www.cnn.com/2020/12/05/health/us-hospitals-covid-pandemic/index.html', config=config, memoize_articles=False)Accessing this temporary directory in macOS can be accomplished in the following matter via the terminal.
cd <path from $TMPDIR>
cd .newspaper_scraper/
cd memoized
ls
www.cnn.com.txt
cat www.cnn.com.txt
https://www.cnn.com/business/media
https://www.cnn.com/travel/news
https://www.cnn.com/2020/12/04/entertainment/mariah-carey-christmas-special/index.html
https://www.cnn.com/2020/12/04/entertainment/your-honor-review/index.html
https://www.cnn.com/2020/12/04/entertainment/star-wars-animation-column/index.html
https://www.cnn.com/2020/12/05/entertainment/lgbtq-holiday-movies-trnd/index.html
https://www.cnn.com/2020/12/04/entertainment/saturday-night-live-jason-bateman/index.html
https://www.cnn.com/2020/12/03/entertainment/blackpink-concert-trnd/index.html
Newspaper3k currently supports 37 different languages, as of October 2020.
import newspaper
newspaper.languages()
Your available languages are:
input code full name
ar Arabic
be Belarusian
bg Bulgarian
da Danish
de German
el Greek
en English
es Spanish
et Estonian
fa Persian
fi Finnish
fr French
he Hebrew
hi Hindi
hr Croatian
hu Hungarian
id Indonesian
it Italian
ja Japanese
ko Korean
lt Lithuanian
mk Macedonian
nb Norwegian (Bokmål)
nl Dutch
no Norwegian
pl Polish
pt Portuguese
ro Romanian
ru Russian
sl Slovenian
sr Serbian
sv Swedish
sw Swahili
th Thai
tr Turkish
uk Ukrainian
vi Vietnamese
zh ChineseThe example below is querying the China Daily news site in the Chinese language. Newspaper3k uses the Chinese Words Segementation Utility jieba when extracting data elements. This Python module was continually building a prefix dict, which displayed build information. Currently the only mechanism to suppress this build information is with this setting jieba.setLogLevel(logging.ERROR).
from newspaper import Config
from newspaper import Article
import jieba
import logging
jieba.setLogLevel(logging.ERROR)
USER_AGENT = 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10.15; rv:78.0) Gecko/20100101 Firefox/78.0'
config = Config()
config.browser_user_agent = USER_AGENT
config.request_timeout = 10
base_url = 'http://tech.chinadaily.com.cn/a/202009/30/WS5f7414f1a3101e7ce9727a44.html'
article = Article(base_url, config=config, language='zh')
article.download()
article.parse()
article_meta_data = article.meta_data
print(article.title)
**发布高分多模卫星首批影像成果
print(article.publish_date)
2020-09-30 00:00:00
article_keywords = {value for (key, value) in article_meta_data.items() if key == 'Keywords'}
if article_keywords:
print(article_keywords)
{'多模,高分,影像,卫星,成果,发布,**'}The example below is querying the Die Zeit news site in the German language. Newspaper3k has some difficulties querying and extracting content from this news site. To bypass these issues, this example uses the Python requests module to query Die Zeit and passes the HTML to Newspaper3k and BeautifulSoup for processing.
This example was written in response to this Newspaper issue: "Add support for zeit.de", which was posted on 09-08-2020.
import json
import requests
from newspaper import Article
from newspaper.utils import BeautifulSoup
HEADERS = {'user-agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10.15; rv:78.0) Gecko/20100101 Firefox/78.0',
'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8'}
base_url = 'https://www.zeit.de/politik/ausland/2020-10/us-wahl-donald-trump-gewalt-milizen-protest'
raw_html = requests.get(base_url, headers=HEADERS, timeout=10)
article = Article('', language='de')
article.download(input_html=raw_html.content)
article.parse()
soup = BeautifulSoup(article.html, 'html.parser')
zeit_dictionary = json.loads("".join(soup.findAll("script", {"type": "application/ld+json"})[3].contents))
date_published = [value for (key, value) in zeit_dictionary.items() if key == 'datePublished']
print(date_published)
['2020-10-12T04:53:14+02:00']
article_author = [value['name'] for (key, value) in zeit_dictionary.items() if key == 'author']
print(article_author)
['Rieke Havertz']
article_title = [value for (key, value) in zeit_dictionary.items() if key == 'headline']
print(article_title)
['US-Wahl: Gewalt nicht ausgeschlossen']
article_summary = [value for (key, value) in zeit_dictionary.items() if key == 'description']
print(article_summary)
['Tote bei Protesten zwischen Linken und Rechten, Terrorpläne im eigenen Land: Die Gewaltbereitschaft in den USA ist vor der Wahl hoch. Und der Präsident deeskaliert nicht.']The example below is querying the Al Arabiya news site in the Arabic language. This example was written in response to this Newspaper issue: "Does not fetch arabic news," which was posted on 01-16-2021. The OP (original poster) could not get Newspaper to extract news content from the Al Arabiya website. The primary reason for Newspaper not being to extract content was because of a cookie acknowledgement button and subscribe button. Both these buttons require an end-user to click them before browsing the website either manually or with automated techniques. To bypass these button an end-user using automated techniques would need to use additional Python modules, such as Scrapy or Selenium..
The code example below is using Selenium, BeautifulSoup and Newspaper. During testing I noted that the subscribe button has random visibility on the page. I attempted to deal with this in my code, but I'm sure that section can be improved upon. It's also worth noting that I could not get Selenium to pass the browser.page_source to either Newspaper Source or newspaper.build. Because of this I passed browser.page_source to BeautifulSoup.
As of 01-21-2021 the code example below worked. I did not fully validate the articles content being extraction, because I do not speak Arabic. Additionally individual article element can be extracted from either the META tags or Javascript section of each specific article page. That code can be easily added with examples provided in this overview document.
import sys
from selenium import webdriver
from selenium.webdriver.common.by import By
from selenium.webdriver.chrome.options import Options
from selenium.webdriver.support.ui import WebDriverWait
from selenium.common.exceptions import WebDriverException
from selenium.common.exceptions import NoSuchElementException
from selenium.webdriver.support import expected_conditions as EC
from selenium.webdriver.common.action_chains import ActionChains
from bs4 import BeautifulSoup
from newspaper import Article
from newspaper import Config
# config details for newspaper
USER_AGENT = 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10.15; rv:78.0) Gecko/20100101 Firefox/78.0'
config = Config()
config.browser_user_agent = USER_AGENT
config.request_timeout = 10
def get_chrome_webdriver():
chrome_options = Options()
chrome_options.add_argument("--test-type")
chrome_options.add_argument('--ignore-certificate-errors')
chrome_options.add_argument('--disable-extensions')
chrome_options.add_argument('disable-infobars')
chrome_options.add_argument("--incognito")
# chrome_options.add_argument('--headless')
# window size as an argument is required in headless mode
chrome_options.add_argument('window-size=1920x1080')
driver = webdriver.Chrome('/usr/local/bin/chromedriver', options=chrome_options)
return driver
def get_chrome_browser(url):
browser = get_chrome_webdriver()
browser.get(url)
return browser
def chrome_browser_teardown(browser):
browser.close()
browser.quit()
return
def bypass_popup_warnings(browser):
try:
hidden_element = WebDriverWait(browser, 120).until(EC.presence_of_element_located((By.ID, "wzrk-cancel")))
if hidden_element.is_displayed():
browser.implicitly_wait(20)
subscribe_button = browser.find_element_by_xpath("//*[@id='wzrk-cancel']")
ActionChains(browser).move_to_element(subscribe_button).click(subscribe_button).perform()
browser.implicitly_wait(20)
cookie_button = browser.find_element_by_xpath("//span[@onclick='createCookie()']")
ActionChains(browser).move_to_element(cookie_button).click(cookie_button).perform()
return True
else:
browser.implicitly_wait(20)
cookie_button = browser.find_element_by_xpath("//span[@onclick='createCookie()']")
ActionChains(browser).move_to_element(cookie_button).click(cookie_button).perform()
return True
except NoSuchElementException:
print('Webdriver is unable to identify the requested element during runtime.')
sys.exit(1)
except WebDriverException:
print('The Element Click command could not be completed because the element receiving the events is obscuring the element that was requested clicked.')
sys.exit(1)
def query_al_arabiya_news(browser):
news_urls = []
soup = BeautifulSoup(browser.page_source, 'lxml')
for a in soup.find_all('a', href=True):
if str(a['href']).startswith('/ar/'):
news_urls.append(f"https://www.alarabiya.net/{a['href']}")
for url in news_urls:
article = Article(url, config=config, language='ar')
article.download()
article.parse()
# additional code required to extract article elements
# please review the page source to determine the techniques
# needed
print(article.title)
return True
news_browser = get_chrome_browser('https://www.alarabiya.net')
warnings_closed = bypass_popup_warnings(news_browser)
if warnings_closed is True:
finished = query_al_arabiya_news(news_browser)
if finished is True:
chrome_browser_teardown(news_browser)Writing data to a comma-separated values (CSV) file is a very common practice in Python. The example below extracts content from a Wall Street Journal article. The items extracted include; the publish date for the article, the authors of this article, the title and summary for this article and the associated keywords assigned to this article. All these data elements are written to an external CSV file. All the data elements were normalized into string variables, which made for easier storage in the CSV file.
import csv
from newspaper import Config
from newspaper import Article
USER_AGENT = 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10.15; rv:78.0) Gecko/20100101 Firefox/78.0'
config = Config()
config.browser_user_agent = USER_AGENT
config.request_timeout = 10
base_url = 'https://www.wsj.com/articles/investors-are-betting-corporate-earnings-have-turned-a-corner-11602408600?mod=hp_lead_pos1'
article = Article(base_url, config=config)
article.download()
article.parse()
article_meta_data = article.meta_data
published_date = {value for (key, value) in article_meta_data.items() if key == 'article.published'}
article_published_date = " ".join(str(x) for x in published_date)
authors = sorted({value for (key, value) in article_meta_data.items()if key == 'author'})
article_author = ', '.join(authors)
title = {value for (key, value) in article_meta_data.items() if key == 'article.headline'}
article_title = " ".join(str(x) for x in title)
summary = {value for (key, value) in article_meta_data.items() if key == 'article.summary'}
article_summary = " ".join(str(x) for x in summary)
keywords = ''.join({value for (key, value) in article_meta_data.items() if key == 'news_keywords'})
keywords_list = sorted(keywords.lower().split(','))
article_keywords = ', '.join(keywords_list)
with open('wsj_extraction_results.csv', 'a', newline='') as csvfile:
headers = ['date published', 'article authors', 'article title', 'article summary', 'article keywords']
writer = csv.DictWriter(csvfile, delimiter=',', lineterminator='\n', fieldnames=headers)
writer.writeheader()
writer.writerow({'date published': article_published_date,
'article authors': article_author,
'article title': article_title,
'article summary': article_summary,
'article keywords': article_keywords})Writing data to a JSON file is also very common practice in Python. The example below extracts content from a Wall Street Journal article. The items extracted include; the publish date for the article, the authors of this article, the title and summary for this article, the associated keywords assigned to this article and the URL of the article. All these data elements are written to an external JSON file. All the data elements were normalized into string variables, which made for easier storage in the JSON file.
import json
from newspaper import Config
from newspaper import Article
news_extraction_results = {}
USER_AGENT = 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10.15; rv:78.0) Gecko/20100101 Firefox/78.0'
config = Config()
config.browser_user_agent = USER_AGENT
config.request_timeout = 10
base_url = 'https://www.wsj.com/articles/investors-are-betting-corporate-earnings-have-turned-a-corner-11602408600?mod=hp_lead_pos1'
article = Article(base_url, config=config)
article.download()
article.parse()
article_meta_data = article.meta_data
published_date = {value for (key, value) in article_meta_data.items() if key == 'article.published'}
article_published_date = " ".join(str(x) for x in published_date)
authors = sorted({value for (key, value) in article_meta_data.items()if key == 'author'})
article_author = ', '.join(authors)
title = {value for (key, value) in article_meta_data.items() if key == 'article.headline'}
article_title = " ".join(str(x) for x in title)
summary = {value for (key, value) in article_meta_data.items() if key == 'article.summary'}
article_summary = " ".join(str(x) for x in summary)
keywords = ''.join({value for (key, value) in article_meta_data.items() if key == 'news_keywords'})
keywords_list = sorted(keywords.lower().split(','))
article_keywords = ', '.join(keywords_list)
news_extraction_results['wsj'] = []
news_extraction_results['wsj'].append({
'published_date': article_published_date,
'authors': article_author,
'summary': article_summary,
'keywords': article_keywords,
'source url': article.url})
# write JSON file
with open('wsj.json', 'w') as json_file:
json.dump(news_extraction_results, json_file)
# read JSON file
with open('wsj.json') as json_file:
data = json.load(json_file)
print(json.dumps(data, indent=4))
{
"wsj": [
{
"published_date": "2020-10-11T09:30:00.000Z",
"authors": "Karen Langley",
"summary": "Investors are entering third-quarter earnings season with brighter expectations for corporate profits, a bet they hope
will propel the next leg of the stock market\u2019s rally.",
"keywords": "c&e exclusion filter, c&e industry news filter, codes_reviewed, commodity/financial market news,
content types, corporate/industrial news, earnings, equity markets, factiva filters, financial performance",
"source url": "https://www.wsj.com/articles/investors-are-betting-corporate-earnings-have-turned-a-corner-11602408600?mod=hp_lead_pos1"
}
]
}Pandas is a powerful Python module that uses a DataFrame object for data manipulation with integrated indexing. This module allows for the efficient reading and writing of data between in-memory data structures and different formats, including CSV, text files, Microsoft Excel and SQL databases.
The example below extracts content from a Wall Street Journal article. The items extracted include; the publish date for the article, the authors of this article, the title and summary for this article and the associated keywords assigned to this article. All these data elements are written to an in-memory data structure. It's worth noting that all these data elements were normalized into string variables, which made for easier storage in the pandas DataFrame.
import pandas as pd
from newspaper import Config
from newspaper import Article
USER_AGENT = 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10.15; rv:78.0) Gecko/20100101 Firefox/78.0'
config = Config()
config.browser_user_agent = USER_AGENT
config.request_timeout = 10
base_url = 'https://www.wsj.com/articles/investors-are-betting-corporate-earnings-have-turned-a-corner-11602408600?mod=hp_lead_pos1'
article = Article(base_url, config=config)
article.download()
article.parse()
article_meta_data = article.meta_data
published_date = {value for (key, value) in article_meta_data.items() if key == 'article.published'}
article_published_date = " ".join(str(x) for x in published_date)
authors = sorted({value for (key, value) in article_meta_data.items()if key == 'author'})
article_author = ', '.join(authors)
title = {value for (key, value) in article_meta_data.items() if key == 'article.headline'}
article_title = " ".join(str(x) for x in title)
summary = {value for (key, value) in article_meta_data.items() if key == 'article.summary'}
article_summary = " ".join(str(x) for x in summary)
keywords = ''.join({value for (key, value) in article_meta_data.items() if key == 'news_keywords'})
keywords_list = sorted(keywords.lower().split(','))
article_keywords = ', '.join(keywords_list)
# pandas DataFrame used to store the extraction results
df_wsj_extraction = pd.DataFrame(columns=['date_published', 'article authors', 'article title',
'article summary', 'article keywords'])
df_wsj_extraction = df_wsj_extraction.append({'date_published': article_published_date,
'article authors': article_author,
'article title': article_title,
'article summary': article_summary,
'article keywords': article_keywords}, ignore_index=True)
print(df_wsj_extraction.to_string(index=False))Newspaper3k has a threading model named news_pool. This function can be used to extract data elements from mutiple sources. The example below is querying articles on CNN and the Wall Street Journal.
Some caveats about using news_pool:
-
Time intensive process - it can take minutes to build the sources, before data elements can be extracted.
-
Additional Erroneous content - newspaper.build is designed to extract all the URLs on a news source, so some of the items parsed needed to be filtered.
-
Redundant content - duplicate content is possible without adding additional data filtering.
-
Different data structures - querying mutiple sources could present problems, especially if the news sources use different data structures, such as summaries being in meta-tags on one site and in script tag on the other site.
This example was written in response to this Newspaper issue: "Multithread extraction seems to fail at the news_pool.join section", which was posted on 08-28-2020.
import newspaper
from newspaper import Config
from newspaper import news_pool
USER_AGENT = 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10.15; rv:78.0) Gecko/20100101 Firefox/78.0'
config = Config()
config.browser_user_agent = USER_AGENT
config.request_timeout = 10
wsj_news = newspaper.build('https://www.wsj.com/', config=config, memoize_articles=False, language='en')
cnn_news = newspaper.build('https://www.cnn.com/', config=config, memoize_articles=False, language='en')
news_sources = [wsj_news, cnn_news]
# the parameters number_threads and thread_timeout_seconds are adjustable
news_pool.config.number_threads = 4
news_pool.config.thread_timeout_seconds = 1
news_pool.set(news_sources)
news_pool.join()
article_urls = set()
for source in news_sources:
for article_extract in source.articles:
if article_extract.url not in article_urls:
article_urls.add(article_extract.url)
print(article_extract.title)Threading is also possible in Newspaper3k by calling various parameters when using the Source architecture to query a news source. This method is less time intensive than the news_pool threading model.
from newspaper import Config
from newspaper import Source
USER_AGENT = 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10.15; rv:78.0) Gecko/20100101 Firefox/78.0'
config = Config()
config.browser_user_agent = USER_AGENT
config.request_timeout = 10
wsj_news = Source(url='https://www.wsj.com/', config=config, memoize_articles=False, language='en',
number_threads=20, thread_timeout_seconds=2)
cnn_news = Source(url='https://www.cnn.com', config=config, memoize_articles=False, language='en',
number_threads=20, thread_timeout_seconds=2)
news_sites = [cnn_news, wsj_news]
for site in news_sites:
site.build()
for article_extract in site.articles:
article_extract.download()
article_extract.parse()
print(article_extract.title)Newspaper3k can extract the text of articles, but the embedded extraction methodology used by Newspaper has numerous problems. For instance every news source has its own unique coding structure and article tag hierarchy, thus Newspaper has difficulty navigating and parsing some sites. In some circumstances Newspaper will either overlook entire sections of an article or unknowingly extract text that does not belong to the article being parsed. Newspaper will also occasionally extract some image tag text for photos linked to associated with an article. I would highly recommended reviewing the textual information extracted by Newspaper prior to performing any Natural Language Processing(NLP) tasks.
Concerning Newspaper3k Natural Language Processing capabilities. The embedded NLP capabilities in my opinion should not be used until the module's owner greatly improves them.
This repository contains a script that can be used to perform various Natural Language Processing tasks on extracted textual information. Feel free to make suggestions to improve this script.
from newspaper import Config
from newspaper import Article
USER_AGENT = 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10.15; rv:78.0) Gecko/20100101 Firefox/78.0'
config = Config()
config.browser_user_agent = USER_AGENT
config.request_timeout = 10
url = 'https://www.newsweek.com/facebook-super-spreader-election-misinformation-1543306'
article = Article(url, config=config)
article.download()
article.parse()
# the replace is used to remove newlines
article_text = article.text.replace('\n', '')
print(article_text)
Less than a week ahead of the U.S. presidential election, misinformation relating to voting and
election security is flourishing on Facebook, despite the platform's pledge to curb such content,
a NewsGuard investigation has found. NewsGuard has identified 40 Facebook pages that are
"super-spreaders" of election-related misinformation, meaning that they have shared false content
about voting or the electoral process to their audiences of at least 100,000 followers. Only three
of the 53 posts we identified on these pages—which together reach approximately 22.9 million
followers—were flagged by Facebook as false. Four of the pages have managers based outside the
U.S.—in Mexico,Vietnam, Australia, and Israel—despite the pages' focus on American politics.
The myths identified by NewsGuard include false claims of mail-in ballots getting thrown away,
narratives that dead people's cast ballots count as votes, and false claims about poll watchers.
The claims about poll watchers cut both ways, with players on both the right and the left pushing
their own, self-serving myths, NewsGuard found.NewsGuard's analysis also found that election-related
myths often seize on routine and solvable voting errors as examples of malpractice or deception,
sowing distrust in the electoral process. Others seem based on either an unintentional or willful
misunderstanding of rules and practices.The false stories NewsGuard identified sometimes included
multiple election myths, while other articles did not fit neatly with one particular election myth.
Nevertheless, all the articles NewsGuard identified advanced inaccurate information about the voting
process. For example, one popular Facebook post recently claimed that Pennsylvania had rejected
372,000 ballots, when in fact, Pennsylvania officials had actually rejected 372,000 ballot applications.
The rejection of absentee ballot applications is not uncommon, nor is it necessarily evidence of anything
untoward. Moreover, a registered voter whose application to vote by mail was rejected can still vote in
person. This falsehood appeared in an article published on 100Percent FedUp.com, a NewsGuard Red-rated
(or generally unreliable) site. Patty McMurray, the co-owner of the site and the author of the article,
told NewsGuard that her site had corrected the article to reflect the distinction between ballots and
ballot applications. However, the false, uncorrected post remains accessible on Facebook and appears on
at least five large Facebook pages. This claim was one of dozens that Facebook did not flag as false.
When a Utah county accidentally sent out 13,000 absentee ballots without a signature line, the NewsGuard
Red-rated site LawEnforcementToday.com called this a "cheat-by-mail scheme." The Salt Lake Tribune reported
that the Sanpete County Clerk quickly learned of the mistake, which was a printing error, and immediately
put information online explaining to voters how to correctly submit their ballot. There was no evidence
that the mistake was part of a voter fraud scheme. But on October 15, the post was shared to three connected
Facebook pages, with a total reach of 1.1 million followers. None of the posts were marked as false by
Facebook's fact-checkers.Conspiratorial stories abounded, with articles warning of violence or other disastrous
and unlawful election outcomes with no evidence to support their claims. Greg Palast, a liberal investigative
journalist, predicted that 6 million people will vote by mail in Florida, but claimed their votes will likely
not be counted. "The GOP-controlled Florida Legislature will say, we can't count them in time, so we're not
going to certify the election," Palast wrote, suggesting this move would be part of a ploy to send the decision
to the U.S. House, which under the 12th Amendment decides the president if no majority is reached in the electoral
college.There is no evidence to suggest that the Florida legislature will refuse to certify the state's results.
This article, shared on Facebook to Palast's 109,000 followers, was not flagged as false by Facebook. The three
Facebook posts that were flagged by fact-checkers did not include such warnings until after the myth had been
published and shared, due to the platform's practice of not providing advance warnings to users about pages that
have been known to publish misinformation or hoaxes in the past. Had such warnings existed, Facebook users would
have known in advance that they might be exposed to misinformation when reading those pages' posts.Despite Facebook's
announced efforts to stop the spread of this type of misinformation, these pages continue to be allowed to publish
blatant misinformation about voting and the electoral process — seemingly in violation of the platform's content
policies. New false stories emerge daily, with inaccurate and deceptive interpretations of events that are perfectly
normal. The result is that Facebook has exposed tens of millions of Americans to falsehoods about America's
electoral process.from newspaper import Config
from newspaper import Article
from utilities.nlp_utilities import NLPCustomMethods
nlp = NLPCustomMethods()
USER_AGENT = 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10.15; rv:78.0) Gecko/20100101 Firefox/78.0'
config = Config()
config.browser_user_agent = USER_AGENT
config.request_timeout = 10
url = 'https://www.newsweek.com/facebook-super-spreader-election-misinformation-1543306'
article = Article(url, config=config)
article.download()
article.parse()
# the replace is used to remove newlines
article_text = article.text.replace('\n', '')
remove_stopwords = nlp.expunge_stopwords(article_text)
normalize_text = nlp.expunge_punctuations(remove_stopwords)
most_common_words = nlp.get_most_common_words(normalize_text, 20)
print(most_common_words)
[('facebook', 15), ('false', 9), ('newsguard', 8), ('pages', 8), ('election', 6), ('misinformation', 6),
('voting', 5), ('identified', 5), ('electoral', 5), ('ballots', 5), ('shared', 4), ('process', 4),
('myths', 4), ('claims', 4), ('ballot', 4), ('evidence', 4), ('article', 4), ('site', 4), ('platform', 3),
('content', 3)]
# this output was sorted() and put into a set()
# noun types can also be tweak under NLPCustomMethods().get_nouns
nouns = nlp.get_nouns(normalize_text)
print(nouns)
['absentee', 'advance', 'amendment', 'americans', 'analysis', 'anything', 'application', 'applications',
'article', 'articles', 'audiences', 'author', 'ballot', 'ballots', 'certify', 'cheatbymail', 'claims',
'clerk', 'content', 'coowner', 'count', 'county', 'curb', 'deception', 'decides', 'decision', 'distinction',
'dozens', 'efforts', 'election', 'error', 'errors', 'events', 'evidence', 'example', 'facebook', 'fact',
'factcheckers', 'falsehood', 'falsehoods', 'falsewhen', 'flag', 'florida', 'followers', 'fraud', 'hoaxes',
'house', 'information', 'interpretations', 'investigation', 'journalist', 'lawenforcementtodaycom', 'legislature',
'line', 'mail', 'majority', 'malpractice', 'managers', 'mcmurray', 'meaning', 'mexicovietnam', 'millions',
'misinformation', 'misunderstanding', 'move', 'myth', 'myths', 'narratives', 'officials', 'online', 'others',
'pages', 'palast', 'part', 'pennsylvania', 'people', 'person', 'platform', 'players', 'pledge', 'policies',
'politicsthe', 'poll', 'post', 'posts', 'practice', 'president', 'printing', 'process', 'processfor', 'reading',
'reflect', 'refuse', 'result', 'results', 'rules', 'scheme', 'security', 'send', 'signature', 'site', 'spread',
'state', 'stories', 'superspreaders', 'support', 'tens', 'time', 'tribune', 'users', 'violation', 'violence',
'vote', 'voter', 'voters', 'votes', 'voting', 'warnings', 'watchers', 'ways', 'week']
# this output was sorted() and put into a set()
# verb types can also be tweak under NLPCustomMethods().get_verbs
verbs = nlp.get_verbs(normalize_text)
print(verbs)
['abounded', 'allowed', 'announced', 'appeared', 'appears', 'articles', 'australia', 'based', 'called',
'cast', 'claim', 'claimed', 'connected', 'continue', 'corrected', 'counted', 'cut', 'electionrelated',
'emerge', 'examples', 'existed', 'explaining', 'exposed', 'fit', 'flagged', 'flourishing', 'focus', 'found',
'foundnewsguard', 'getting', 'going', 'greg', 'identified', 'include', 'known', 'learned', 'least', 'left',
'mailin', 'marked', 'mistake', 'october', 'outcomes', 'ploy', 'poll', 'postsdespite', 'practicesthe', 'predicted',
'providing', 'published', 'pushing', 'put', 'reach', 'reached', 'redrated', 'rejected', 'rejection', 'relating',
'remains', 'reported', 'salt', 'say', 'seem', 'seize', 'selfserving', 'sent', 'shared', 'sowing', 'stop',
'submit', 'suggesting', 'thrown', 'told', 'vote', 'voting', 'warning', 'wrote']
word_frequency = nlp.get_frequency_distribution(normalize_text, 20)
print(word_frequency)
[('facebook', 15), ('false', 9), ('newsguard', 8), ('pages', 8), ('election', 6), ('misinformation', 6),
('voting', 5), ('identified', 5), ('electoral', 5), ('ballots', 5), ('shared', 4), ('process', 4),
('myths', 4), ('claims', 4), ('ballot', 4), ('evidence', 4), ('article', 4), ('site', 4), ('platform', 3),
('content', 3)]Newspaper3k has the capabilities to create a summary of the article text, but newspaper does not have the flexibility to tweak the the process.
The example directly below shows how to use summarization with newspaper. The article being summarized is part of The Guardian's "Long Read" essays. The article's title is The curse of 'white oil': electric vehicles' dirty secret and its length is approximately 4400 words. Newspaper is designed to summarize to 5 lines, which in this case is around 107 words.
from newspaper import Config
from newspaper import Article
USER_AGENT = 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10.15; rv:78.0) Gecko/20100101 Firefox/78.0'
config = Config()
config.browser_user_agent = USER_AGENT
config.request_timeout = 10
base_url = 'https://www.theguardian.com/news/2020/dec/08/the-curse-of-white-oil-electric-vehicles-dirty-secret-lithium'
article = Article(base_url, config=config)
article.download()
article.parse()
article.nlp()
print(article.summary)
The sudden excitement surrounding petróleo branco (“white oil”) derives from an invention rarely seen in these parts: the electric car.
More than half (55%) of global lithium production last year originated in just one country: Australia.
The Portuguese government is preparing to offer licences for lithium mining to international companies in a bid to exploit its “white oil” reserves.
As manufacture has slowed down, a glut of lithium on global markets has dampened the white oil boom, if only temporarily.
If people were better informed, he reasoned, it’s just possible that public opinion could swing to their side, and the country’s lithium mining plans could get shelved.The example below uses the Python library sumy, which is an automatic text summarizer. Sumy has multiple algorithms that can be used to summarize text. The summarizer being used in ths example is LexRank, which using the PageRank algorithm in an unsupervised approach. LexRank creates a summary with 151 words.
from newspaper import Article
from sumy.utils import get_stop_words
from sumy.nlp.stemmers import Stemmer
from sumy.nlp.tokenizers import Tokenizer
from sumy.parsers.plaintext import PlaintextParser
from sumy.summarizers.lex_rank import LexRankSummarizer as Summarizer
LANGUAGE = "english"
# configurable number of sentences
SENTENCES_COUNT = 5
article = Article('https://www.theguardian.com/news/2020/dec/08/the-curse-of-white-oil-electric-vehicles-dirty-secret-lithium')
article.download()
article.parse()
# text cleaning
text = "".join(article.text).replace("\n", " ").replace('"', "").replace("• Follow the Long Read on Twitter at @gdnlongread, and sign up to the long read weekly email here.", "")
parser = PlaintextParser.from_string(text, Tokenizer(LANGUAGE))
stemmer = Stemmer(LANGUAGE)
summarizer = Summarizer(stemmer)
summarizer.stop_words = get_stop_words(LANGUAGE)
article_summary = []
for sentence in summarizer(parser.document, SENTENCES_COUNT):
article_summary.append(str(sentence))
clean_summary = ' '.join([str(elem) for elem in article_summary])
print(clean_summary)
Savannah is just one of several mining companies with an eye on the rich lithium deposits of central and northern Portugal. A series of local and national protests, including a march in Lisbon last year, sought to raise awareness about the impacts of modern mining on the natural environment, including potential industrial-scale habitat destruction, chemical contamination and noise pollution, as well as high levels of water consumption. The extra materials and energy involved in manufacturing a lithium-ion battery mean that, at present, the carbon emissions associated with producing an electric car are higher than those for a vehicle running on petrol or diesel – by as much as 38%, according to some calculations. In the case of Savannah’s mine in northern Portugal, the company concedes there will be local environmental impact, but argues that it will be outweighed by the upsides (inward investment, jobs, community projects). These interior regions need investment.The Guardian's article The curse of 'white oil': electric vehicles' dirty secret is about the environmental impact of mining lithium for electric vehicles. The sumy summarization seems to be more accurate than newspaper's summarization for the same article.
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