def search(self, lang, queries, articles_per_query=10, should_break=None, on_progress=None):
""" Searches for articles.
Args:
lang(str): A language code in ISO 639-1 format.
queries(list of str): A list of queries.
should_break (callback): Callback for breaking the computation before the end.
If it evaluates to True, downloading is stopped and document downloaded till now
are returned in a Corpus.
on_progress (callable): Callback for progress bar.
"""
wikipedia.set_lang(lang)
results = []
for i, query in enumerate(queries):
try:
articles = wikipedia.search(query, results=articles_per_query)
for j, article in enumerate(articles):
if callable(should_break) and should_break():
break
results.extend(self._get(article, query, should_break))
if callable(on_progress):
on_progress((i*articles_per_query + j+1) / (len(queries) * articles_per_query),
len(results))
except (wikipedia.exceptions.HTTPTimeoutError, IOError) as e:
self.on_error(str(e))
break
if callable(should_break) and should_break():
break
return Corpus.from_documents(results, 'Wikipedia', self.attributes,
self.class_vars, self.metas, title_indices=[-1])
def create_corpus(self):
return Corpus.from_documents(self.tweets,
'Twitter',
self.attributes,
self.class_vars,
self.metas,
title_indices=[-1])
def create_corpus(self):
""" Creates a corpus with collected tweets. """
self.statuses_lock.acquire()
corpus = Corpus.from_documents(self.tweets, 'Twitter', self.attributes,
self.class_vars, self.metas, title_indices=[-2])
self.statuses_lock.release()
return corpus
def create_corpus(self, search_author):
if search_author:
class_vars = self.authors
metas = self.metas
else:
class_vars = []
metas = self.metas + self.authors
return Corpus.from_documents(self.tweets, 'Twitter', self.attributes,
class_vars, metas,
title_indices=[-1])
def search(self, apikey, query, articles_per_query=100):
""" Searches for articles."""
er = EventRegistry(apiKey=apikey)
q = QueryArticles(keywords=query)
articles = {}
results = []
q.setRequestedResult(
RequestArticlesInfo(
page=1,
count=articles_per_query,
returnInfo=ReturnInfo(
articleInfo=ArticleInfoFlags(duplicateList=True,
concepts=True,
categories=True,
location=True,
socialScore=True,
sentiment=True,
dates=True))))
res = er.execQuery(q)
articles = res['articles']
results = articles['results']
simplized_results = []
for result in results:
simplized_result = {}
author = ''
if len(result['authors']) == 0:
author = 'None'
else:
author = result['authors'][0]['name']
simplized_result['title'] = result['title']
simplized_result['date'] = result['date']
simplized_result['time'] = result['date']
simplized_result['dataType'] = result['date']
simplized_result['author'] = result['date']
simplized_result['url'] = result['date']
simplized_result['dateCrawl'] = result['date']
simplized_result['timeCrawl'] = result['date']
simplized_result['sentiment'] = result['date']
simplized_result['body'] = result['date']
simplized_results += simplized_result
return Corpus.from_documents(simplized_results,
'EventRegistry',
self.attributes,
self.class_vars,
self.metas,
title_indices=[-1])
def search(self,
lang,
queries,
articles_per_query=10,
should_break=None,
on_progress=None):
""" Searches for articles.
Args:
lang(str): A language code in ISO 639-1 format.
queries(list of str): A list of queries.
should_break (callback): Callback for breaking the computation before the end.
If it evaluates to True, downloading is stopped and document downloaded till now
are returned in a Corpus.
on_progress (callable): Callback for progress bar.
"""
wikipedia.set_lang(lang)
results = []
for i, query in enumerate(queries):
try:
articles = wikipedia.search(query, results=articles_per_query)
for j, article in enumerate(articles):
if callable(should_break) and should_break():
break
results.extend(self._get(article, query, should_break))
if callable(on_progress):
on_progress((i * articles_per_query + j + 1) /
(len(queries) * articles_per_query),
len(results))
except (wikipedia.exceptions.HTTPTimeoutError, IOError) as e:
self.on_error(str(e))
break
if callable(should_break) and should_break():
break
return Corpus.from_documents(results,
'Wikipedia',
self.attributes,
self.class_vars,
self.metas,
title_indices=[-1])
def corpus(*texts):
metas = [(data.StringVariable('text'), lambda doc: doc['text'])]
d = [{'text': t} for t in texts]
c = Corpus.from_documents(d, 'example', [], [], metas, [-1])
c.set_text_features([metas[0][0]])
return c
def create_corpus(self):
return Corpus.from_documents(self.tweets, 'Twitter', self.attributes,
self.class_vars, self.metas,
title_indices=[-1])
