def get_source(s,p):
results = db_session.query(Item).filter(Item.source == s).order_by(Item.share_count.desc()).all()
response_left = results[10*(p-1):10*p]
print('Total %d hits found.' % len(results))
for h in response_left:
h.polarity = round(h.polarity,2)
h.subjectivity = round(h.subjectivity,2)
pagination = Pagination(page=p, href= URL + 'source/' + s + '/item/{0}/',
total=len(results), search=False, record_name='item')
return render_template('source.html',css_framework='bootstrap',
jsondata_left=response_left,pagination=pagination)
def input(): req = json.loads(request.data.decode()) try: try: u = db_session.query(User).filter_by(uid=str(req['uid'])).one() except NoResultFound: u = User(uid=req['uid'], screen_name=req['screen_name'], followers_count=req['followers_count'], friends_count=req['friends_count'], statuses_count=req['statuses_count'], rank=req['rank']) db_session.add(u) db_session.commit() tw = Item(message=req['message'], contestant=req['contestant'], item_id=req['item_id'], group_item_id=req['group_item_id'], # for expanded url item_type=req['item_type'], item_url=req['item_url'], location=req['location'], date=req['date'], # all time is stored at UTC source=req['source'], sentiment=req['sentiment'], sentiment_textblob=req['sentiment_textblob'], sentiment_bayes=req['sentiment_bayes'], polarity=req['polarity'], # tbc subjectivity=req['subjectivity'], # tbc favorite_count=req['favorite_count'], share_count=req['share_count'], user_id=u.id, verified_user=req['verified_user'], team=req['team'], data=req['data']) db_session.add(tw) db_session.commit() # words for w in req['words']: if len(w)>100: continue w_obj = Word(word=w) db_session.add(w_obj) db_session.commit() tw.words.append(w_obj) u.words.append(w_obj) # hashtags for t in req['hashtags']: if len(t)>100: continue t_obj = Hashtag(hashtag=t) db_session.add(t_obj) db_session.commit() tw.hashtags.append(t_obj) u.hashtags.append(t_obj) # url if req['expanded_url'] and len(req['expanded_url'])<200: url = Url(item_id=req['item_id'], url=req['expanded_url']) db_session.add(url) db_session.commit() except OperationalError: db_session.rollback() print (tw.id) return str(tw.id)
def on_data(self, data):
# sometimes tweepy send NoneType objects
if data is None:
return
# decode json
dict_data = json.loads(data)
if "user" not in dict_data:
print ("invalid format: no user found, skip." + "\n")
return
# check if retweet/quote/retweet-quote
retweeted_quoted_status = False
retweeted_status = bool("retweeted_status" in dict_data)
if retweeted_status:
retweeted_quoted_status = bool("quoted_status" in dict_data["retweeted_status"])
quoted_status = bool("quoted_status" in dict_data)
# check if duplication and skip tweets with users with less than req threshold
if retweeted_quoted_status:
if dict_data["retweeted_status"]["quoted_status"]["user"]["followers_count"]<MIN_FOLLOWERS or dict_data["retweeted_status"]["quoted_status"]["user"]["friends_count"]<MIN_FRIENDS:
print ("less than user metric threshold for storage, skip." + "\n")
return
print ('retweeted_quoted_status')
key_fields = dict_data["retweeted_status"]["quoted_status"]
elif retweeted_status:
if dict_data["retweeted_status"]["user"]["followers_count"]<MIN_FOLLOWERS or dict_data["retweeted_status"]["user"]["friends_count"]<MIN_FRIENDS:
print ("less than user metric threshold for storage, skip." + "\n")
return
print ('retweeted_status')
key_fields = dict_data["retweeted_status"]
elif quoted_status:
if dict_data["quoted_status"]["user"]["followers_count"]<MIN_FOLLOWERS or dict_data["quoted_status"]["user"]["friends_count"]<MIN_FRIENDS:
print ("less than user metric threshold for storage, skip." + "\n")
return
print ('quoted_status')
key_fields = dict_data["quoted_status"]
else:
if dict_data["user"]["followers_count"]<MIN_FOLLOWERS or dict_data["user"]["friends_count"]<MIN_FRIENDS:
print ("less than user metric threshold for storage, skip." + "\n")
return
print ('normal_status')
key_fields = dict_data
id_str = key_fields["id_str"]
tweet = key_fields["text"]
if retweeted_quoted_status or retweeted_status:
favorite_count = dict_data["retweeted_status"]["favorite_count"]
share_count = dict_data["retweeted_status"]["retweet_count"]
else:
favorite_count = key_fields["favorite_count"]
share_count = key_fields["retweet_count"]
user_id = key_fields["user"]["id"]
screen_name = key_fields["user"]["screen_name"]
location = key_fields["user"]["location"]
followers_count = key_fields["user"]["followers_count"]
friends_count = key_fields["user"]["friends_count"]
statuses_count = key_fields["user"]["statuses_count"]
date = time.strftime('%Y-%m-%dT%H:%M:%S', time.strptime(key_fields["created_at"],'%a %b %d %H:%M:%S +0000 %Y'))
item_url = 'https://twitter.com/' + str(screen_name) + '/status/' + str(id_str)
# minumum threshold by elapsed time
timedelta = datetime.datetime.utcnow()-datetime.datetime.strptime(date,'%Y-%m-%dT%H:%M:%S')
minutes_elapsed = td_to_minutes(timedelta)
if minutes_elapsed<20:
minimum_rt = 3*minutes_elapsed
elif minutes_elapsed<120:
minimum_rt = 80*math.log(minutes_elapsed)-180
else:
minimum_rt = 233
important = 'f'
if share_count>minimum_rt:
important = 't'
# check if item already stored
try:
record = db_session.query(Item).filter(Item.item_id==id_str).one()
if quoted_status or retweeted_quoted_status:
# quoted tweets contain no new data
print ('Quoted / Retweeted_quoted tweet caught. No update. ID: ' + str(id_str) + '\n')
return
record.favorite_count=favorite_count
record.share_count=share_count
db_session.commit()
# publish to pubnub
pubnub_object = ({'sentiment': record.sentiment, 'group_item_id': record.group_item_id,
'item_id': id_str, 'source': 'twitter', 'favorite_count': favorite_count,
'share_count': share_count, 'contestant': record.contestant,
'item_url': item_url, 'date': date, 'important': important})
pubnub.publish(channel='pubnub-sng',message=pubnub_object)
print ('Retweet caught. Updated favorite and share count record. ID: ' + str(id_str) + '\n')
return
except NoResultFound:
pass
# queue storing previous 500 tweet texts for duplication check
if tweet[:20] in queue:
print ('Tweet already processed, repeat found in queue, skip.' + '\n')
return
queue.append(tweet[:20])
if len(queue)>500:
queue.popleft()
# preprocessing tweet in nlp_textblob
try:
expanded_url = str(dict_data["entities"]["urls"][0]["expanded_url"]).lower()
except IndexError:
expanded_url = None
tweet_dict = analyse_tweet(tweet,expanded_url)
if tweet_dict['contestant'] is None:
print ('CONTESTANT NOT FOUND' + '\n')
return
words = clean_words(tweet_dict['tb_words'],tweet_dict['hashtags'],tweet_dict['shoutouts'])
# distant supervised tweet classification bayes probability
sentiment_bayes = accumulate_prob(hash_wordcount,words)
# for group_item_id
url_record = db_session.query(Url).filter(Url.url==expanded_url).first()
try:
group_item_id = url_record.item_id
except AttributeError:
group_item_id = id_str
# check tweet is from verified candidate twitter account
if str(screen_name) in CANDIDATE_USERNAMES[tweet_dict['contestant']]['UserName']:
print ('VERIFIED USER')
verified = True
important = 't'
else:
verified = False
# publish to pubnub
pubnub_object = ({'sentiment': tweet_dict['sentiment'], 'group_item_id': group_item_id,
'item_id': id_str, 'source': 'twitter', 'favorite_count': favorite_count,
'share_count': share_count, 'contestant': tweet_dict['contestant'],
'item_url': item_url, 'date': date, 'important': important})
pubnub.publish(channel='pubnub-sng',message=pubnub_object)
# select correct fields
if "media" not in dict_data["entities"]:
item_type = "text"
else:
if dict_data["entities"]["media"][0]["type"] == "photo":
item_type = "image"
else:
item_type = dict_data["entities"]["media"][0]["type"]
# output key fields
print (str(screen_name) + ' My score: ' + str(tweet_dict['sentiment']) + ' TB score: ' + tweet_dict['tb_sentiment'] + ' Bayes score: ' + sentiment_bayes)
print ('Tweet ID: ' + str(id_str) + ' ' + str(minutes_elapsed) + ' minutes ago')
print ('Friends Count: ' + str(friends_count) + ' Followers Count: ' + str(followers_count))
print ('Retweet Count: ' + str(share_count) + ' Favorite Count: ' + str(favorite_count))
print (str(tweet))
print (item_url)
# send to server
item_data = ({'uid':user_id,
'screen_name':screen_name,
'followers_count':followers_count,
'friends_count':friends_count,
'statuses_count':statuses_count,
'rank': 1,
'message': tweet,
'contestant': tweet_dict['contestant'],
'item_id': id_str,
'group_item_id': group_item_id,
'item_type': item_type,
'item_url': item_url,
'location': location,
'date': date,
'source': "twitter",
'sentiment': tweet_dict['sentiment'],
'sentiment_textblob': tweet_dict['tb_sentiment'],
'sentiment_bayes': sentiment_bayes,
'polarity': tweet_dict['tb_polarity'],
'subjectivity': tweet_dict['tb_subjectivity'],
'favorite_count': favorite_count,
'share_count': share_count,
'verified_user': verified,
'team': tweet_dict['team'],
'data': json.dumps(data),
'words': words,
'hashtags': tweet_dict['hashtags'],
'expanded_url': expanded_url})
headers = {'Content-type': 'application/json', 'Accept': 'text/plain'}
r = requests.post(URL + 'api/input_data/', data=json.dumps(item_data), headers=headers)
print (r)
print(r.text + '\n')
return True