def crawling_news(company_name_list, start_date, end_date, save_file_name):
#set logger Handler
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
stream_handler = logging.StreamHandler()
logger.addHandler(stream_handler)
#define googlenews
googlenews = GoogleNews(lang='en',
start=start_date,
end=end_date,
encode='utf-8')
#news.google.com search sample
all_title = []
logging.info('loop start')
for i in range(len(company_name_list)):
comp_name = company_name_list[i]
googlenews.search(comp_name)
logging.info('%s : %d%s' %
(comp_name,
((i + 1) / len(company_name_list)) * 100, '%'))
for j in range(len(googlenews.results())):
temp = []
temp.append(googlenews.results()[j].get('title'))
temp.append(comp_name)
temp.append(fixing_date(googlenews.results()[j].get('date')))
all_title.append(temp)
#clear result list
googlenews.clear()
all_title = pd.DataFrame(all_title)
all_title.to_csv('%s.csv' % (save_file_name))
logging.info('saved as %s.csv, done!!' % (save_file_name))
return all_title
def getNews(query):
googleNews = GoogleNews()
googleNews.search(query)
news = []
i = 0
number = min([len(googleNews.result()), 6])
for result in googleNews.result():
if (i > number):
break
n = {}
n["title"] = result['title']
n["description"] = result['desc']
n["link"] = result['link']
if (i == 0):
n["image"] = result['img']
news.append(n)
i += 1
googleNews.clear()
return news
def job(self):
#Download current database
self.getDB()
self.print_header(self.rawFileName)
self.lineCounter(self.rawFileName)
x = 0
for tag in self.newsTags:
#print("Collecting newses from tag: " + tag + "...")
self.logger.info(f"Collecting newses from tag: {tag}")
googlenews = GoogleNews()
googlenews.clear()
googlenews.set_lang(self.newsLang)
googlenews.setperiod('1d')
googlenews.get_news(tag)
output = googlenews.results(sort=True)
output = pd.DataFrame(output)
x = x + len(output['title'])
self.saveToFile(output, self.rawFileName)
self.logger.info(f"Collected amount of news: {x}")
self.removeDuplicates(self.rawFileName, self.finalFileName)
#os.remove(rawFileName) #delete bufor file
#logger.info(f"Removed file with duplicates: {rawFileName}")
os.rename(self.finalFileName, self.rawFileName) #rename final file to bufor name
self.logger.info(f"Renamed: {self.finalFileName} to: {self.rawFileName}")
self.backupDB()
def googleNewsCrawler(self):
result_list = []
googlenews = GoogleNews()
for i in range(self.__numDays):
startDateTime = self.__dateTime + timedelta(days=i)
endDateTime = self.__dateTime + timedelta(days=i + self.__daysSpan)
googlenews.setTimeRange(
start=str(startDateTime.month) + '/' + str(startDateTime.day) +
'/' + str(startDateTime.year),
end=str(endDateTime.month) + '/' + str(endDateTime.day) + '/' +
str(endDateTime.year))
googlenews.search(self.__keyWords)
for j in range(self.__pagsEveryDay - 1):
googlenews.getpage(j + 2)
logging.info(
str(self.__keyWords + '__' + str(startDateTime.date()) +
" append " + str(int(self.__pagsEveryDay * 10)) +
" items"))
result_list = result_list + googlenews.result()
googlenews.clear()
if (i + 1) % 10 == 0:
self.toJson(result_list)
result_list = []
continue
self.toJson(result_list)
def get_company_news_link(company='NaN', news_num=5, time_range='today'):
if company == 'NaN':
return 'please input company name'
news_link = []
googlenews = GoogleNews()
googlenews.clear()
if time_range != 'today':
start_date = "{1}/{2}/{0}".format(time_range[0:4], time_range[5:7],
time_range[8:10])
end_date = "{1}/{2}/{0}".format(time_range[11:15], time_range[16:18],
time_range[19:21])
googlenews.set_time_range(start_date, end_date)
googlenews.search(company)
result = googlenews.result()
try:
for num in range(news_num):
news_link.append(result[num]['link'])
except IndexError:
if len(news_link) == 0:
return '此時段無' + company + '新聞 OR 網路不穩'
return news_link
else:
return news_link
def extract_google(query_terms, startDate, endDate):
if len(startDate) == 0:
startDate = datetime.datetime.today().strftime('%d/%m/%Y')
if len(endDate) == 0:
endDate = datetime.datetime.strftime(
datetime.datetime.today().date() - datetime.timedelta(days=7),
'%d/%m/%Y')
startDate = datetime.datetime.strptime(startDate,
'%Y-%m-%d').strftime('%d/%m/%y')
endDate = datetime.datetime.strptime(endDate,
'%Y-%m-%d').strftime('%d/%m/%y')
final_articles = []
print(startDate)
print(endDate)
print("Crawling Starting")
# here extracting news from google news
googlenews = GoogleNews()
googlenews.setTimeRange(startDate, endDate)
for query in query_terms:
googlenews.clear()
#forming the search term
googlenews.search("India Technology " + query)
result = googlenews.result()
for n in range(len(result)):
source = result[n]['media']
url = result[n]['link']
try:
article = Article(url)
article.download()
article.parse()
except Exception as e:
print("Trouble downloading so skipping")
continue
content = article.text
# summarize the content
temp_content = re.sub(r'^\s*[\(\[].*?[\)\]]\s*', '', content)
sentences = sent_detector.tokenize(temp_content)
summary = (" ".join(sentences[:2]).strip())
date = result[n]['date']
if (date.find('ago') != -1):
date = current.date()
title = result[n]['title']
# content=result[n]['desc']
img = result[n]['img']
#adding the extracted info in final_articles list
final_articles.append({
'source': source,
'url': url,
'date': date,
'title': title,
'content': content,
'img': img
})
return final_articles
def get_news(text):
googlenews = GoogleNews()
googlenews.search(text)
googlenews.clear()
googlenews.getpage(2)
result = googlenews.result()
return result
def search_google_news(query, google_date):
#-- Retrieve news articles
# Init googlenews
googlenews = GoogleNews()
#googlenews.set_period('7d') # Cannot use set_period with set_time_range, use either or.
#googlenews.set_time_range(str(google_date), '2020-10-12')
googlenews.set_encode('utf-8')
googlenews.search(query)
googlenews.getpage(50)
result = googlenews.result()
# Clear before searching again
googlenews.clear()
return result
def getNews(text):
googlenews = GoogleNews("en", "m" )
#Parameters: language and timeframe (day, month, year)
googlenews.search(text)
googlenews.getpage(1)
#first Result Page
headlines = googlenews.gettext()
#only show Headlines (pictures or links are also available)
#convert to string, Because Wordcloud expects string like object
text = ' '.join([str(elem) for elem in headlines])
generateWordCloud(text)
googlenews.clear()
return
def get_news(text): googlenews = GoogleNews() googlenews.search(text) googlenews.clear() googlenews.getpage(2) result = googlenews.result() for index in result[0]: # print(index, '\n', result[0][index]) if index == "title": title = result[0][index] elif index == "link": link = result[0][index] domain = get_domain(link) else: continue return title, link, domain
async def search(ctx, *, message):
googlenews = GoogleNews(lang='en', period='d')
googlenews.search(message)
result = googlenews.gettext()
embed = discord.Embed()
embed.colour = discord.Colour.from_rgb(255, 225, 135)
embed.set_author(name="Google NEWS",
icon_url="https://i.imgur.com/tDLGRiT.jpg")
embed.set_footer(
text="Data from Google News | WallSt Bot made by Bruno Lazaro.")
embed.add_field(
name=f"{message} News",
value=
(f'{result[1]}\n\n{result[2]}\n\n{result[3]}\n\n{result[4]}\n\n{result[5]}\n\n{result[6]}'
),
inline=False)
await ctx.send(embed=embed)
googlenews.clear()
def get_links(self, pages=1):
"""obtains all relevant links from the search,
for each company.
Args:
pages :: int
number of google pages to search resuts from
Stores:
links :: dict(list[dict])
dictionaries of list, keys being search terms
and values being relevant information (e.g. URL)
"""
gnews = GoogleNews(start=self.date_from, end=self.date_to)
links = {}
#obtaining all the URLs
for s in self.search_terms:
gnews.search(s)
for p in range(1, pages + 1):
gnews.getpage(p)
result = gnews.result() #stores values until cleared
links[s] = result
gnews.clear()
#removing irrelevant links
for s in self.search_terms:
tmp = []
num = dd[s] #number of relevant terms in search_terms
rel_str = ' '.join(s.lower().split()[:num]) #relevant string
for d in links[s]:
#selection criterion, e.g. if search term
#is 'apple news', then want to subset based on 'apple' rather than 'apple news'
#--> filter with first word of each search term
if rel_str in d['desc'].lower():
tmp.append(d)
links[s] = tmp
self.search_info = links
return None
def detailedNews():
name = request.form["companyName"]
googlenews = GoogleNews()
googlenews.clear()
googlenews.search(name)
newsresult = googlenews.result(sort=True)
if 'logged_in' in session:
time1 = datetime.now()
unm = session["username"]
db = pymysql.connect(host="localhost",
user="******",
password="",
database="stock")
cursor = db.cursor()
sqlnews = "select * from newshistory where username= %s "
if ((cursor.execute(sqlnews, (unm)) != 0)):
db.commit()
newshistory = cursor.fetchall()
count1 = len(newsresult)
if (count1 > 5):
count1 = 5
else:
count1 = count1
try:
with db.cursor() as cursor:
sql = "insert into newshistory(newsname,username,time)values(%s,%s,%s)"
cursor.execute(sql, (name, unm, time1))
db.commit()
finally:
db.close()
return render_template('DetailedNews.html',
title='Display News',
l=newsresult,
year=datetime.now().year,
name=name,
newshistory=newshistory,
count1=count1)
def get_corpus_in_time_interval(start_time, end_time, args):
query = args.query
page_count = args.pages
gn = GoogleNews(start=start_time, end=end_time)
corpus = list()
gn.search(query)
for i in range(1, page_count + 1):
gn.clear()
gn.getpage(i)
all_rel_news = gn.result()
for raw_news in all_rel_news:
news = News(raw_news)
if i == 1:
news.set_relv()
if news.mainText != 'fail':
corpus.append(news)
return corpus
def scrape_the_news():
user_agent = 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/50.0.2661.102 Safari/537.36'
config = Config()
config.browser_user_agent = user_agent
topiclist = NLP_news()
print(topiclist[0])
googlenews = GoogleNews()
googlenews.set_lang('en')
googlenews.set_encode('utf-8')
googlenews.set_period('7d')
googlenews.get_news(topiclist[0])
result = googlenews.results()
googlenews.clear()
df = pd.DataFrame(result)
df = df.drop(['date', 'media'], axis=1)
df.columns = ['Date', 'Summary', 'Image', 'Link', 'Site', 'Title']
df = df[['Title', 'Summary', 'Image', 'Link', 'Date', 'Site']]
conn = psycopg2.connect("dbname=EdTech user=postgres password=edtech123")
curr = conn.cursor()
for i, row in df.iterrows():
try:
row.Link = 'https://' + row.Link
columns = row.keys()
values = [row[column] for column in columns]
insert_statement = "INSERT INTO scrapenews_newslist VALUES (nextval('scrapenews_newslist_id_seq'::regclass),%s, %s, %s, %s, %s, %s)"
curr.execute(insert_statement, tuple(values))
except:
print('could not add row', i)
conn.commit()
curr.close()
conn.close()
def getNews(topic, start_time, end_time):
googlenews = GoogleNews(start=start_time, end=end_time)
titles = []
texts = []
labels = []
for i in range(1, 2):
googlenews.clear()
googlenews.search(topic)
googlenews.getpage(i)
tmp = googlenews.result()
#result += [x["title"]+x["desc"] for x in tmp]
(tmp_title, tmp_text) = get_content(tmp)
titles += tmp_title
texts += tmp_text
if i == 1:
labels += [1 for _ in range(len(tmp_text))]
else:
labels += [0 for _ in range(len(tmp_text))]
#labels = np.array(labels)
return (titles, texts, labels)
def googlenews_function(keyword='台積電',
language='cn',
start_date='2020/12/01',
end_date='2020/12/28'):
'''
- 日期
- 關鍵字
- 語言
- 爬幾頁
'''
googlenews = GoogleNews()
googlenews.clear()
googlenews.set_encode('utf-8')
googlenews.set_lang(language)
all_date_start = start_date.split('/')
start_year = all_date_start[0]
start_month = all_date_start[1]
start_day = all_date_start[2]
all_date_start = '{}/{}/{}'.format(start_month, start_day, start_year)
all_date_end = end_date.split('/')
end_year = all_date_end[0]
end_month = all_date_end[1]
end_day = all_date_end[2]
all_date_end = '{}/{}/{}'.format(end_month, end_day, end_year)
googlenews.set_time_range(start=all_date_start, end=all_date_end)
googlenews.search(keyword)
data = googlenews.result()
print("資料總筆數:", len(data))
news = pd.DataFrame(data)
# news.to_csv("GoogleNews_" + keyword +"_日期" + start_date.replace('/', '-') + '到' +end_date.replace('/', '-')+ ".csv", index= False)
return news
def getNews(query):
googleNews = GoogleNews()
googleNews.search(query)
news = []
i = 0
number = min([len(googleNews.result()), 7])
for result in googleNews.result():
if (i > number):
break
news.append({
"title": result['title'],
"description": result['desc'],
"link": result['link'],
"date": result['date'],
"image": result['img']
})
googleNews.clear()
return news
class Engine:
def __init__(self):
self.news = GoogleNews()
self.news.setlang('en')
#self.news.setTimeRange('01/01/2000','01/01/2015')
self.news.setencode('utf-8')
self.pageNumber = 1
self.searchTerm = ""
def nextPage(self):
if self.news.result == None:
raise RuntimeError("Engine has not searched yet")
self.pageNumber += 1
self.news.clear()
self.news.getpage(self.pageNumber)
if len(self.news.result()) == 0: return False
else: return True
def previousPage(self):
if self.news.result == None:
raise RuntimeError("Engine has not searched yet")
self.pageNumber -= 1
self.news.clear()
self.news.getpage(self.pageNumber)
if len(self.news.result()) == 0: return False
else: return True
def search(self, term):
self.news.search(term)
if len(self.news.result()) == 0:
return False
else:
return self.news.result()
def getPageNumber(self):
return self.pageNumber
def getResults(self):
return self.news.result()
def clear(self):
self.news.clear()
def resetPageNumber(self):
self.pageNumber = 1
model.load_state_dict(
torch.load('model/best_model_state.bin', map_location='cpu'))
model = model.to(device)
# review_text = input('Enter the review you want to check:\n')
## Google News start
news_content = []
searchInput = input('Enter the search keyword:\n')
googlenews.search(searchInput)
for i in range(1, 1 + 1):
googlenews.getpage(i)
for i in googlenews.result():
news_content.append(i['desc'])
googlenews.clear()
## End
for i in news_content:
encoded_review = tokenizer.encode_plus(
i,
max_length=MAX_LEN,
add_special_tokens=True,
return_token_type_ids=False,
pad_to_max_length=True,
return_attention_mask=True,
return_tensors='pt',
)
input_ids = encoded_review['input_ids'].to(device)
attention_mask = encoded_review['attention_mask'].to(device)
def __extrai_noticias_gnews(q,
dia_inicio,
dia_fim,
num_limite_paginas=1,
lang='pt-BR',
sleep=1,
tentativas=5):
"""
Retorna data frame com as notícias obtidas na aba News do Google
Parâmetros
----------
q : str
String de busca
data_inicio, dta_fim : datatime.Date
Datas de início e fim para realização da busca
num_limite_num_limite_paginas : int
Número máxima de páginas que serão obtidas.
lang : str
Código da lingua para realização da busca (padrão pt-BR)
sleep : int
Número de segundos para esperar entre tentativas após cada erro de obtenção de página
tentativas : int
Número de tentativas de obnteção de uma página antes de se considerar a extração concluída
Retorno
-------
resultados : DataFrame
Dataframe com os reulstados de busca
"""
# String de busca formatado adequadamente para URL
# q = urllib.parse.quote(q)
# Strings com as datas no formato esperado pela lib GoogleNews
formato_data = '%m/%d/%Y'
dia_inicio_formatado = dia_inicio.strftime(formato_data)
dia_fim_formatado = dia_fim.strftime(formato_data)
# Instancia interface de busca ao Google News com idioma pt-BR e período adequado
gn = GoogleNews(lang=lang,
start=dia_inicio_formatado,
end=dia_fim_formatado)
# Inicializa lista para armazenar os resultados de busca
resultados = []
# Realiza busca da primeira página
logger = logging.getLogger('covidata')
logger.info(f'Buscando página 1')
gn.search(q)
resultados = resultados + gn.result()
gn.clear()
# Para a página 2 em diante (p2 corresponde ao índice 1)
for i in range(2, num_limite_paginas + 1):
logger.info(f'Buscando página {i}')
# Busca a página
gn.getpage(i)
# Adiciona reusltado à lista
resultados = resultados + gn.result()
# Caso a consulta à página não tenha gerado resultados
if gn.result() == []:
logger.info(
f'A consulta à página {i} não retornou nehnum resultado')
# Diminui o contador de tentaivas
tentativas = tentativas - 1
logger.info(f'*** Há {tentativas} restantes ***')
# Caso o número de tentativas tenha chegado a zero, interrompe a execução
if tentativas < 1:
break
# Caso contrário
else:
# Pausa script por sleep segundos antes de buscar a próxima página
logger.info(f'Execução interrompida por {sleep} segundos')
time.sleep(sleep)
# Apaga cache do resultado
gn.clear()
# Cria e retorna dataframe
return pd.DataFrame(resultados)
main_topic = input('Choose a topic: ')
while run:
try:
gnews.search(main_topic)
for n, result in enumerate(gnews.result()):
print(n, result['title'])
## choose which article to pick
article = input('\nChoose an article by its main index or choose [all] to visualize all links: ')
if article == 'all':
[print(f'{n}: {new}') for n, new in enumerate(gnews.gettext())]
print('--')
[print(f'{n}: {link}') for n, link in enumerate(gnews.get__links())]
else:
article = int(article)
print(f'Article - Title: {gnews.gettext()[article]}')
list_artcl = gnews.gettext()
print(f'Article - Link: {gnews.get__links()[article]}')
list_links = gnews.get__links()
print('==========================================================================\n')
go_further = input('DO you want to read other articles? (y/n) ').lower()
if go_further == 'y' or go_further == 'yes':
gnews.clear() ## before going in loop, needs to clears the article list
else:
run = False ## break the loop
except:
print('Error :S')
run = False ## break the loop
def run(self):
googlenews = GoogleNews()
calList = self.genCalList(self.start,self.end)
posL,negL,neuL,comL = [],[],[],[]
pageCount = 10
ALL_RESULT = {}
t = time.time()
for date in calList:
print(f'\n\n{date}')
PREV_RES = []
RESULT = {}
continu = True
for page in range(1,pageCount+1):
t1 = time.time()
googlenews = GoogleNews(start=date, end=date, lang='en')
googlenews.search(self.word)
googlenews.getpage(page)
results = googlenews.result()
googlenews.clear()
if results == []:
continu = False
break
results = [dict(t) for t in {tuple(d.items()) for d in results}]
results = [i for i in results if i not in PREV_RES]
if len(results) < 1:
break
for res in results:
RESULT[res['title']] = res['link']
PREV_RES = results
print(f"Page: {page}. Name: {self.word}. t={round(time.time()-t1,2)}s")
if not os.path.exists(f'./news/data/{self.word}'):
os.mkdir(f'./news/data/{self.word}')
old_data = {}
if os.path.isfile(f'./news/data/{self.word}/{self.word}.json'):
with open(f'./news/data/{self.word}/{self.word}.json','r') as JSON:
old_data = json.load(JSON)
JSON.close()
if continu == False:
print('No results.')
with open(f'./news/data/{self.word}/{self.word}.json','w') as JSON:
old_data[date] = {}
json.dump(old_data,JSON)
JSON.close()
continue
titles = [i for i in RESULT.keys()]
links = [i for i in RESULT.values()]
body = self.genBody(links)
allWords = [self.word] + self.synonyms
print('\nFetching <p> text...')
oldText = self.genAllText(body,allWords)
allText = [i for i in oldText if i != '']
print(f"Texts aquired of total: {(len(allText)/len(oldText))*100}%")
if allText == []:
with open(f'./news/data/{self.word}/{self.word}.json','w') as JSON:
old_data[date] = {}
json.dump(old_data,JSON)
JSON.close()
continue
print('Sentiment analysis...')
pos,neg,neu,com,count = self.measureSentiment(allText)
pos_,neg_,neu_,com_ = self.avSentiment(pos,neg,neu,com,count)
posL += [pos_]
negL += [neg_]
neuL += [neu_]
comL += [com_]
formatted = {'synonyms':self.synonyms,'pos':pos_,'neg':neg_,'neu':neu_,'com':com_,'raw text':allText,'sample size':len(RESULT),'page count':pageCount}
old_data[date] = formatted
with open(f'./news/data/{self.word}/{self.word}.json','w') as JSON:
json.dump(old_data,JSON)
JSON.close()
print(time.time()-t)
def parse(self):
#for the number of news sources you have loop through them
for i in range(len(self.news_sources)):
if self.news_sources[i] == 'NewsYCombinator':
#access the website and find all the stories on the front page
self.markup.append(
requests.get('https://news.ycombinator.com/').text)
soup = BeautifulSoup(self.markup[i], 'html.parser')
links = soup.findAll("a", {"class": "storylink"})
self.saved_links = []
#search all stories on front page to find out whether or not your key words are there
for link in links:
for keyword in self.keywords:
if keyword in link.text:
self.saved_links.append(link)
#get all of the links and save them, then declare that articles have been found
for a in range(len(self.saved_links)):
self.read_links.append(str(self.saved_links[a]['href']))
self.hasArticles = True
elif self.news_sources[i] == 'NewYorkTimes':
#To get your api key go to nyt developers website create an account and create an app, then select search api and copy your key from there
api = articleAPI('API KEY')
#loop through all key words to find out whether or not articles have them
for a in range(len(self.keywords)):
if (datetime.datetime.now().day - 1 > 0):
articles = api.search(
q=self.keywords[a],
begin_date=datetime.datetime.now().year * 10000 +
(datetime.datetime.now().month) * 100 +
(datetime.datetime.now().day - 1),
page=1)
elif (datetime.datetime.now().month - 1 == 4
or datetime.datetime.now().month - 1 == 6
or datetime.datetime.now().month - 1 == 9
or datetime.datetime.now().month - 1 == 11):
articles = api.search(
q=self.keywords[a],
begin_date=datetime.datetime.now().year * 10000 +
(datetime.datetime.now().month - 1) * 100 +
(datetime.datetime.now().day + 29),
page=1)
elif (datetime.datetime.now().month - 1 == 2
and datetime.datetime.now().year % 4 == 0):
articles = api.search(
q=self.keywords[a],
begin_date=datetime.datetime.now().year * 10000 +
(datetime.datetime.now().month - 1) * 100 +
(datetime.datetime.now().day + 28),
page=1)
elif (datetime.datetime.now().month - 1 == 2):
articles = api.search(
q=self.keywords[a],
begin_date=datetime.datetime.now().year * 10000 +
(datetime.datetime.now().month - 1) * 100 +
(datetime.datetime.now().day + 28),
page=1)
else:
articles = api.search(
q=self.keywords[a],
begin_date=datetime.datetime.now().year * 10000 +
(datetime.datetime.now().month - 1) * 100 +
(datetime.datetime.now().day + 30),
page=1)
self.list_of_articles = []
for docs in articles['response']['docs']:
article_blurbs = {}
article_blurbs = docs.get('headline').get(
'main') + '\n' + docs.get(
'web_url') + '\n' + docs.get('snippet')
self.list_of_articles.append(str(article_blurbs))
#if has an article, declare articles have been found
if len(self.list_of_articles) > 0:
self.hasArticles = True
elif self.news_sources[i] == 'GoogleNews':
googlenews = GoogleNews()
googlenews = GoogleNews(lang='en')
googlenews = GoogleNews(
start=str(datetime.datetime.now().month) + '/' +
str(datetime.datetime.now().day - 1) + '/' +
str(datetime.datetime.now().year),
end=str(datetime.datetime.now().month) + '/' +
str(datetime.datetime.now().day) + '/' +
str(datetime.datetime.now().year))
self.googleArticles = [[] for z in range(len(self.keywords))]
for a in range(len(self.keywords)):
googlenews.search(self.keywords[a])
gnews = googlenews.result()
for docs2 in gnews:
self.googleArticles[a].append(
str(docs2.get('title')) + '\n' +
str(docs2.get('date')) + '\n' +
str(docs2.get('link')) + '\n' +
str(docs2.get('desc')))
googlenews.clear()
if len(self.googleArticles) > 0:
self.hasArticles = True
