def url_path(self):
if os.path.getsize(self.file_path) == 0:
with open(self.file_path, "w+", encoding="utf-8") as uf:
popular_urls_list = newspaper.popular_urls()
for i in range(len(popular_urls_list)):
uf.write(popular_urls_list[i])
uf.write("\n")
print("populate url list have worte")
def get_trending():
url = ('https://newsapi.org/v2/top-headlines?'
'country=us&'
'apiKey=bcaaf0d008994d818672b2c1141be98b')
response = requests.get(url)
popular_articles = {
'articles': json.loads(response.text).get('articles')
}
popular_url_topics = {
'popular_urls': newspaper.popular_urls(),
'hot_topics': newspaper.hot()
}
return {**popular_articles, **popular_url_topics}
def trending():
trending_terms = newspaper.hot()
trending_urls = newspaper.popular_urls()[:10]
return trending_terms, trending_urls
def test_popular_urls(self):
"""
just make sure this runs
"""
newspaper.popular_urls()
def test_popular_urls(self):
"""
just make sure this runs
"""
newspaper.popular_urls()
def get_popular():
global pop
pop = newspaper.popular_urls()
def get_popular_urls(self, *args, **kwargs):
return newspaper.popular_urls()
def test_popular_urls(self):
"""Just make sure this method runs
"""
newspaper.popular_urls()
def newspaper_stories(words,
search_type='or',
search_level=0,
urls=None,
display=True,
memorize=False,
language='en'):
config = newspaper.Config()
config.memoize_articles = memorize
config.language = language
config.fetch_images = False
config.request_timeout = 20
config.MIN_WORD_COUNT = 300
config.MIN_SENT_COUNT = 10
if urls == None or urls == 'top_news':
news_urls = {
'huffington': 'http://huffingtonpost.com',
'reuters': 'http://www.reuters.com',
'cbs-news': 'http://www.cbsnews.com',
'usa-today': 'http://usatoday.com',
'cnn': 'http://cnn.com',
'npr': 'http://www.npr.org',
'abc-news': 'http://abcnews.com',
'us-news': 'http://www.usnews.com',
'msn': 'http://msn.com',
'pbs': 'http://www.pbs.org',
'nbc-news': 'http://www.nbcnews.com',
'msnbc': 'http://www.msnbc.com',
'fox': 'http://www.foxnews.com'
}
elif urls == 'all_us_news':
news_urls = {
'abc-news': 'https://abcnews.go.com',
'al-jazeera-english': 'http://www.aljazeera.com',
'ars-technica': 'http://arstechnica.com',
'associated-press': 'https://apnews.com/',
'axios': 'https://www.axios.com',
'bleacher-report': 'http://www.bleacherreport.com',
'bloomberg': 'http://www.bloomberg.com',
'breitbart-news': 'http://www.breitbart.com',
'business-insider': 'http://www.businessinsider.com',
'buzzfeed': 'https://www.buzzfeed.com',
'cbs-news': 'http://www.cbsnews.com',
'cnbc': 'http://www.cnbc.com',
'cnn': 'http://us.cnn.com',
'crypto-coins-news': 'https://www.ccn.com',
'engadget': 'https://www.engadget.com',
'entertainment-weekly': 'http://www.ew.com',
'espn': 'http://espn.go.com',
'espn-cric-info': 'http://www.espncricinfo.com/',
'fortune': 'http://fortune.com',
'fox-news': 'http://www.foxnews.com',
'fox-sports': 'http://www.foxsports.com',
'google-news': 'https://news.google.com',
'hacker-news': 'https://news.ycombinator.com',
'ign': 'http://www.ign.com',
'mashable': 'http://mashable.com',
'medical-news-today': 'http://www.medicalnewstoday.com',
'msnbc': 'http://www.msnbc.com',
'mtv-news': 'http://www.mtv.com/news',
'national-geographic': 'http://news.nationalgeographic.com',
'national-review': 'https://www.nationalreview.com/',
'nbc-news': 'http://www.nbcnews.com',
'new-scientist': 'https://www.newscientist.com/section/news',
'newsweek': 'http://www.newsweek.com',
'new-york-magazine': 'http://nymag.com',
'next-big-future': 'https://www.nextbigfuture.com',
'nfl-news': 'http://www.nfl.com/news',
'nhl-news': 'https://www.nhl.com/news',
'politico': 'https://www.politico.com',
'polygon': 'http://www.polygon.com',
'recode': 'http://www.recode.net',
'reddit-r-all': 'https://www.reddit.com/r/all',
'reuters': 'http://www.reuters.com',
'techcrunch': 'https://techcrunch.com',
'techradar': 'http://www.techradar.com',
'american-conservative':
'http://www.theamericanconservative.com/',
'hill': 'http://thehill.com',
'huffington-post': 'http://www.huffingtonpost.com',
'next-web': 'http://thenextweb.com',
'verge': 'http://www.theverge.com',
'wall-street-journal': 'http://www.wsj.com',
'washington-post': 'https://www.washingtonpost.com',
'washington-times': 'https://www.washingtontimes.com/',
'time': 'http://time.com',
'usa-today': 'http://www.usatoday.com/news',
'vice-news': 'https://news.vice.com',
'wired': 'https://www.wired.com'
}
elif urls == "texas_universities":
news_urls = {
'A&M': 'http://www.tamu.edu',
'A&M-Commerce': 'http://www.tamuc.edu',
'A&M-Corpus': 'http://www.tamucc.edu',
'A&M-Kingsville': 'http://www.tamuk.edu',
'A&M-Galveston': 'http://www.tamug.edu',
'A&M-PrairieView': 'http://www.pvamu.edu',
'A&M-International': 'http://www.tamiu.edu',
'A&M-WestTexas': 'http://www.wtamu.edu',
'Baylor': 'http://www.baylor.edu',
'Rice': 'http://www.rice.edu',
'SFAustin': 'http://www.sfasu.edu',
'SMU': 'http://www.smu.edu',
'SulRoss': 'http://www.sulross.edu',
'TexasState': 'http://www.txstate.edu',
'Texas_Tech': 'http://www.ttu.edu',
'UDallas': 'http://www.udallas.edu',
'UHouston': 'http://www.uh.edu',
'UTexas': 'http://www.utexas.edu',
'UT_Dallas': 'http://www.utdallas.edu',
'UT_ElPaso': 'http://www.utep.edu',
'UT_Houston': 'http://www.uth.edu',
'UT_NorthTexas': 'http://www.unt.edu',
'UT_SanAntonio': 'http://www.utsa.edu'
}
elif urls == 'popular':
news_urls = {}
agency_urls = newspaper.popular_urls()
for i in range(len(agency_urls)):
val = agency_urls[i]
url = agency_urls[i].replace("http://", "")
url = url.replace("www.", "")
url = url.replace("blog.", "")
url = url.replace("blogs.", "")
url = url.replace(".com", "")
url = url.replace(".net", "")
url = url.replace(".au", "")
url = url.replace(".org", "")
url = url.replace(".co.uk", "")
url = url.replace("the", "")
url = url.replace(".", "-")
url = url.replace('usa', 'usa-')
if url == 'berkeley-edu':
continue
if url == 'beta-na-leagueoflegends':
continue
if url == 'bottomline-as-ucsb-edu':
continue
news_urls[url] = val
else:
news_urls = urls
print("\nSearch Level {:<d}:".format(search_level), end="")
if search_level == 0:
print(" Screening URLs for search words")
print(" URLs must contain one or more of:", end="")
else:
print(" No URL Screening")
print(" Deep Search for Articles containing: ", end="")
i = 0
for word in words:
i += 1
if i < len(words):
if search_type == 'or':
print(word + " or ", end="")
else:
print(word + " & ", end="")
else:
print(word)
df_articles = pd.DataFrame(columns=[
'agency', 'url', 'length', 'keywords', 'title', 'summary', 'text'
])
n_articles = {}
today = str(date.today())
for agency, url in news_urls.items():
paper = newspaper.build(url, config=config)
if display:
print("\n{:>6d} Articles available from {:<s} on {:<10s}:".
format(paper.size(), agency.upper(), today))
article_collection = []
for article in paper.articles:
url_lower = article.url.lower()
# Exclude articles that are in a language other then en
# or contains mostly video or pictures
# search_level 0 only downloads articles with at least
# one of the key words in its URL
# search_level 1 download all articles that appear to be
# appear to be in English and are not mainly photos or
# videos.
# With either search level, if an article is downloaded
# it is scanned to see if it contains the search words
# It is also compared to other articles to verify that
# it is not a duplicate of another article.
# Special Filters for some Agencies
if agency == 'cbs-news':
if url_lower.find('.com') >= 0:
# secure-fly are duplicates of http
if article.url.find('secure-fly') >= 0:
continue
if agency == 'usa-today':
if url_lower.find('tunein.com') >= 0:
continue
if agency == 'huffington':
# Ignore huffington if it's not .com
if url_lower.find('.com') < 0:
continue
# Filter Articles that are primarily video, film or not en
if url_lower.find('.video/') >=0 or \
url_lower.find('/video') >=0 or \
url_lower.find('/picture') >=0 or \
url_lower.find('.pictures/')>=0 or \
url_lower.find('/photo') >=0 or \
url_lower.find('.photos/') >=0 or \
url_lower.find('espanol') >=0 or \
url_lower.find('.mx/' ) >=0 or \
url_lower.find('/mx.' ) >=0 or \
url_lower.find('.fr/' ) >=0 or \
url_lower.find('/fr.' ) >=0 or \
url_lower.find('.de/' ) >=0 or \
url_lower.find('/de.' ) >=0 or \
url_lower.find('.it/' ) >=0 or \
url_lower.find('/it.' ) >=0 or \
url_lower.find('.gr/' ) >=0 or \
url_lower.find('/gr.' ) >=0 or \
url_lower.find('.se/' ) >=0 or \
url_lower.find('/se.' ) >=0 or \
url_lower.find('.es/' ) >=0 or \
url_lower.find('/es.' ) >=0 or \
url_lower.find('?button') >=0 or \
url_lower.find('calendar.') >=0 or \
url_lower.find('calendar/') >=0 or \
url_lower.find('/event/') >=0 or \
url_lower.find('engr.utexas') >=0 or \
url_lower.find('sites.smu.') >=0:
continue
# Filter if search_level == 0, URL quick search
if search_level == 0:
# Verify url contains at least one of the key words
found_it = False
for word in words:
j = url_lower.find(word)
if j >= 0:
found_it = True
break
if found_it:
# Article contains words and passes filters
# Save this article for full review
article_collection.append(article.url)
else:
# No URL screening, Save for full review
article_collection.append(article.url)
n_to_review = len(article_collection)
if display:
print("{:>6d} Selected for download".format(n_to_review))
for article_url in article_collection:
article = Article(article_url, config=config)
try:
article.download()
except:
if display:
print("Cannot download:", article_url[0:79])
continue
n = 0
# Limit download failures
stop_sec = 1 # Initial max wait time in seconds
while n < 2:
try:
article.parse()
n = 99
except:
n += 1
# Initiate download again before new parse attempt
article.download()
# Timeout for 5 seconds waiting for download
t0 = time()
tlapse = 0
while tlapse < stop_sec:
tlapse = time() - t0
# Double wait time if needed for next exception
stop_sec = stop_sec + 1
if n != 99:
if display:
print("Cannot download:", article_url[0:79])
n_to_review -= 1
continue
article.nlp()
keywords = article.keywords
title = article.title
summary = article.summary
text = article.text
text_lower_case = text.lower()
if search_type == 'or':
found_it = False
# Verify the url contains at least one of the key words
for word in words:
j = text_lower_case.find(word)
if j >= 0:
found_it = True
break
else:
# search type 'and'
found_it = True
for word in words:
j = text_lower_case.find(word)
if j < 0:
found_it = False
break
if found_it:
# Article contains words and passes filters
# Save this article for later full review
length = len(text)
df_story = pd.DataFrame([[
agency, article_url, length, keywords, title, summary,
text
]],
columns=[
'agency', 'url', 'length',
'keywords', 'title', 'summary',
'text'
])
# Check for an identical already in the file
if df_articles.shape[0] == 0:
df_articles = df_articles.append(df_story)
else:
# Verify this story is not already in df_articles
same_story = False
for i in range(df_articles.shape[0]):
if text == df_articles['text'].iloc[i]:
same_story = True
n_to_review -= 1
continue
if not (same_story):
df_articles = df_articles.append(df_story)
else:
n_to_review -= 1
print("=", end='')
n_articles[agency] = [n_to_review, len(article_collection)]
if display:
print("\n\nArticles Selected by Agency:")
for agency in news_urls:
ratio = str(n_articles[agency][0]) + "/" + \
str(n_articles[agency][1])
ratio = ratio
print("{:>10s} Articles from {:<s}".format(
ratio, agency.upper()))
print("\nArticles Collected on " + today + ":",
df_articles.shape[0], 'from',
df_articles['agency'].nunique(), "Agencies.")
print("\nSize Agency Title")
print("*{:->78s}*".format("-"))
for i in range(df_articles.shape[0]):
k = len(df_articles['title'].iloc[i])
if k > 63:
for j in range(25):
k = 63 - j
if df_articles['title'].iloc[i][k] == " ":
break
print("{:>5d} {:<10s} {:<63s}".format(
df_articles['length'].iloc[i],
df_articles['agency'].iloc[i],
df_articles['title'].iloc[i][0:k]))
if len(df_articles['title'].iloc[i]) > 63:
print(" {:<60s}".format(
df_articles['title'].iloc[i][k:120]))
else:
print("{:>5d} {:<10s} {:<s}".format(
df_articles['length'].iloc[i],
df_articles['agency'].iloc[i],
df_articles['title'].iloc[i]))
print("")
print("*{:->78s}*".format("-"))
return df_articles
aws_access_key_id="enter key",
aws_secret_access_key="enter secret")
# To create a new table, we have to set up the schema
# The schema requires a primary hash key and allows a secondary range key
my_schema = conn.create_schema(hash_key_name='id', hash_key_proto_value=int)
# Create the table by providing a name, schema and the computing power required by AWS
table = conn.create_table(name="newspaper",
schema=my_schema,
read_units=10,
write_units=5)
# To fill our table, we are going to parse through websites that publish news
# The newspaper package happens to contain a list of over 200 popular news websites
sources = newspaper.popular_urls()
hashkey = 1 # primary key integer to be used for DynamoDB table
source_iterator = 0 # iterator to be increased at end of loop to move to next source
for source in sources:
parser = sources[source_iterator]
try:
collect = newspaper.build(
parser) # Build method returns an object with each sub-site url
except:
source_iterator += 1 # If the Build method fails, move on to next source
# Store articles as dict type in a list to iterate through for upload to DynamoDB
collection = []
for article in collect.articles:
def popular_urls():
return newspaper.popular_urls()
import newspaper as nw
text =[]
papers=[]
popular_urls = nw.popular_urls()
for url in popular_urls:
print 'building: ' , url
paper = nw.build(language='en',url=url, memoize_articles=False, )
papers.append(paper)
print 'done..'
#nw.news_pool.set(papers, threads_per_source=2) # (3*2) = 6 threads total
#nw.news_pool.join()
with open('dataset.txt', 'w+') as f:
for paper in papers:
print len(paper.articles)
for article in paper.articles:
article.download()
article.parse()
text.append(article.title)
try:
if article.title!='Something\'s Gone Terribly Wrong':
f.write((article.title.decode('utf-8').encode('cp1250'))+'\n')
except:
pass
def PopularUrl(self):
return newspaper.popular_urls()
def test_popular_urls(self):
"""Just make sure this method runs
"""
newspaper.popular_urls()
# Note you will need to enter your own private keys from AWS
conn = boto.dynamodb.connect_to_region(
'us-west-2',
aws_access_key_id="enter key",
aws_secret_access_key="enter secret")
# To create a new table, we have to set up the schema
# The schema requires a primary hash key and allows a secondary range key
my_schema = conn.create_schema(hash_key_name = 'id', hash_key_proto_value = int)
# Create the table by providing a name, schema and the computing power required by AWS
table = conn.create_table(name="newspaper", schema=my_schema, read_units=10, write_units=5)
# To fill our table, we are going to parse through websites that publish news
# The newspaper package happens to contain a list of over 200 popular news websites
sources = newspaper.popular_urls()
hashkey = 1 # primary key integer to be used for DynamoDB table
source_iterator = 0 # iterator to be increased at end of loop to move to next source
for source in sources:
parser = sources[source_iterator]
try:
collect = newspaper.build(parser) # Build method returns an object with each sub-site url
except:
source_iterator += 1 # If the Build method fails, move on to next source
# Store articles as dict type in a list to iterate through for upload to DynamoDB
collection = []
for article in collect.articles:
try:
import newspaper # hot() returns a list of the top trending terms on Google using a public api print(newspaper.hot()) # popular_urls() returns a list of popular news source urls print(newspaper.popular_urls()) newspaper.languages()
import newspaper
volkskrant = newspaper.build('https://www.volkskrant.nl/', language='nl')
print(volkskrant.size())
for category in volkskrant.category_urls():
print(category)
for article in volkskrant.articles:
print(article.url)
print('\n')
print(newspaper.hot(), end='\n\n')
print(newspaper.popular_urls(), end='\n\n')
print(newspaper.languages())
# url = 'https://www.volkskrant.nl/nieuws-achtergrond/eerste-dode-in-nederland-maar-wat-is-eigenlijk-de-kans-om-aan-corona-te-overlijden~bf716564/'
# article = newspaper.Article(url)
# article.download()
# article.parse()
# print(article.authors)
# article.publish_date
# article.text
# article.top_image
# article.nlp()
# article.keywords
import newspaper
from newspaper import Article
import pandas as pd
data = pd.read_csv('../data/uci-news-aggregator.csv',sep=',')
#while(1):
#print(newspaper.popular_urls())
urls = newspaper.popular_urls()
#urls = ['https://www.wsj.com/news/world/middle-east','http://wsj.com','http://nytimes.com','http://www.bbc.co.uk','http://www.npr.org','http://www.reuters.com','http://www.economist.com','http://www.pbs.org','http://bigstory.ap.org','http://cnn.com','http://www.ted.com','http://www.washingtonpost.com','http://www.newyorker.com','http://www.cbs.com']
#print(data.head(5))
url_test = "https://www.nytimes.com/2020/08/08/business/economy/lost-unemployment-benefits.html?action=click&module=Top%20Stories&pgtype=Homepage"
article = Article(url_test)
article.download()
article.parse()
print(article.text)
exit()
for row in data:
try:
first_article = Article(url=str(row['URL'], language='en'))
print(first_article)
#article = newspaper.build(str(row['URL']), memoize_articles=False, language='en')
#with open('sample'+str(row['ID']), 'w') as f:
except:
print("Error")
'''print("$$$$ Novi url: ",url)
cnn_paper = newspaper.build(str(url), memoize_articles=False, language='en')
print("$$$$ Broj artikala: ",len(cnn_paper.articles))
j = 0
for article in cnn_paper.articles:
try:
j+=1
def popular_paper():
popular_url = newspaper.popular_urls()
return popular_url