def hot_websites(self):
hot_topics = newspaper.hot()
hot_topics_str = []
for topic in hot_topics:
split_topic = topic.lower().split()
hot_topics_str.extend(split_topic)
hot_count = self._scrapper.get_web_count(hot_topics_str, 7)
return hot_count
def hot_topics(self):
hot_topics = newspaper.hot()
hot_topics_str = []
for topic in hot_topics:
split_topic = topic.lower().split()
hot_topics_str.extend(split_topic)
recent_topics = self._scrapper.get_recent_topics(7)
hot_count = self._scrapper.get_hot_count(hot_topics_str, recent_topics)
return hot_count
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 test_hot_trending(self):
"""Grab google trending, just make sure this runs
"""
newspaper.hot()
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()
def test_hot_trending(self):
"""Grab google trending, just make sure this runs
"""
newspaper.hot()
def trending():
trending_terms = newspaper.hot()
trending_urls = newspaper.popular_urls()[:10]
return trending_terms, trending_urls
def trends():
np3k = newspaper.hot()
trend = np3k
return trend
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
def get_hot_trends(self, *args, **kwargs):
return newspaper.hot()
for genre in genres:
wds=nltk.corpus.brown.words(categories=genre)
wd_len=[ len(wd) for wd in wds if wd.isalpha()]
cpw=1.0*sum(wd_len)/len(wd_len)
num_wds=len(wds)
num_sents=len(nltk.corpus.brown.sents(categories=genre))
wps=1.0*num_wds/num_sents
ari=4.71*cpw+0.5*wps-21.43
output=output + [ [genre,ari] ]
return output
# 30 Use the Porter Stemmer to normalize some tokenized text, calling the stemmer on each word.
# Do the same thing with the Lancaster Stemmer and see if you observe any differences.
import newspaper
hot_topics=newspaper.hot()
news_sources=newspaper.popular_urls()
cnn_paper = newspaper.build('http://cnn.com')
my_art=cnn_paper.articles[25]
my_art.download()
my_art.parse()
my_art.nlp()
raw=my_art.text
tokens=nltk.word_tokenize(raw)
port_stem = [ nltk.PorterStemmer().stem(wd).lower() for wd in tokens ]
lanc_stem = [ nltk.LancasterStemmer().stem(wd).lower() for wd in tokens ]
port_set=set(port_stem)
lanc_set=set(lanc_stem)
in_port_not_lanc = port_set-lanc_set
in_lanc_not_port = lanc_set-port_set
def hot():
return newspaper.hot()
def Hot(self):
return newspaper.hot()
def hot_news():
hot_topic = newspaper.hot()
return hot_topic
