def timeline(request):
# this should occur only one time
ttl = twitter.Twitter(auth=twitter.OAuth("607817327-PQcljjGheGvAeGxqpngFdo80FXgm0RDjAHXYPVjT",
"PxyS6Mr3iUuNp4LdsA796V2a23l8VTFnK6nQxEGaPR0",
"xllraX45N5CmMCTQwEDfQ",
"gB07DLx9V6xWUpceunMQ40OuuOANOlDuhZHRnl2RlY"))
clas = Classificator()
clas.gender_train("library/dataset/gender.csv")
bayes = Bayes("library/dataset/training-sentiment/")
# back to normal from here
ht = ttl.statuses.home_timeline()
result = []
for tweet in ht:
result.append(dict(name=tweet['user']['name'],
timestamp=time.strftime('%Y-%m-%d %H:%M:%S', time.strptime(tweet['created_at'],'%a %b %d %H:%M:%S +0000 %Y')),
img=tweet['user']['profile_image_url'],
text=tweet['text'],
screenname=tweet['user']['screen_name'],
gender=clas.gender_classify(tweet['user']['name']),
sentiment=bayes.classify(tweet['text']),
)
)
t = loader.get_template('tweets.html')
c = Context({
'result' : result,
})
return HttpResponse(t.render(c))
def search(request, keyword, numresult=3):
tws = twitter.Twitter(domain="search.twitter.com")
twa = twitter.Twitter(domain="api.twitter.com")
sr = tws.search(q=keyword,rpp=numresult)
clas = Classificator()
clas.gender_train("library/dataset/gender.csv")
bayes = Bayes("library/dataset/training-sentiment/")
# searchresult = [res['text'] for res in searchresult['results']]
result = []
for tweet in sr['results']:
namefromid = twa.users.show(user_id=tweet['from_user_id'])
result.append(dict(name=namefromid['name'],
timestamp=time.strftime('%Y-%m-%d %H:%M:%S',
time.strptime(tweet['created_at'],'%a, %d %b %Y %H:%M:%S +0000')),
img=tweet['profile_image_url'],
text=tweet['text'],
screenname=tweet['from_user'],
gender=clas.gender_classify(namefromid['name']),
sentiment=bayes.classify(tweet['text']),
)
)
t = loader.get_template('tweets.html')
c = Context({
'result' : result,
})
return HttpResponse(t.render(c))
