def main():
filepath = 'example_tweets.txt'
with open(filepath) as fp:
line = fp.readline()
cnt = 1
while line:
line = fp.readline()
if line == '':
continue
print('original message: ' + line.strip())
clean_message = (' '.join(
re.sub("(@[A-Za-z0-9]+)|([^0-9A-Za-z \t])|(\w+:\/\/\S+)", " ",
line).split()))
clean_message2 = (' '.join(
re.sub("(@[A-Za-z0-9]+)", " ", line).split()))
clean_message3 = (' '.join(
re.sub("(@[A-Za-z0-9]+)", " ", line).split()))
clean_message4 = (' '.join(
re.sub("(\w+:\/\/\S+)", " ", line).split()))
clean_message5 = (' '.join(re.sub("(#.+)", " ", line).split()))
clean_message6 = (' '.join(re.sub("(#.*$)", " ", line).split()))
clean_message7 = (' '.join(re.sub("(#.*)", " ", line).split()))
clean_message8 = (' '.join(re.sub("(#\w+)", " ", line).split()))
clean_message9 = (' '.join(
re.sub(
"(#\w+)|(@[A-Za-z0-9]+)|([^0-9A-Za-z \t])|(\w+:\/\/\S+)",
" ", line).split()))
#clean_message3 = (' '.join(re.sub((#text;"#\\w*")"," ", line).splsit())
print('cleaned message: ' + clean_message)
print('cleaned message2: ' + clean_message2)
print('cleaned message3: ' + clean_message3)
print('cleaned message4: ' + clean_message4)
print('cleaned message5: ' + clean_message5)
print('cleaned message6: ' + clean_message6)
print('cleaned message7: ' + clean_message7)
print('cleaned message8: ' + clean_message8)
print('cleaned message9: ' + clean_message9)
print('original sentiment score: ' + str(sentiment_score(line)))
print('cleaned sentiment score: ' +
str(sentiment_score(clean_message)))
analysis = TextBlob(clean_message)
print('cleaned TextBlob sentiment score: ' +
str(analysis.sentiment.polarity))
#print(analysis.sentiment.polarity)
#print('cleaned TextBlob sentiment score: ' + (TextBlob(clean_message.sentiment.polarity)))
print('\n')
cnt += 1
def count_senti(row, counts):
try:
value = row.value
coordinates = value[2]['boundingBox']['coordinates'][0][0]
longitude = coordinates[0]
latitude = coordinates[1]
language = value[3]
if language != 'en':
return counts
city = get_geo(latitude, longitude)
if city == '':
return counts
score = sentiment_score(value[0])
except:
return counts
data_city = counts.get(city, {})
data_city['totalTweet'] = data_city.get('totalTweet', 0) + 1
data_city['totalSenti'] = data_city.get('totalSenti', 0) + score
if score > 0.1:
data_city['totalPos'] = data_city.get('totalPos', 0) + 1
data_city.setdefault('totalNeg', 0)
else:
data_city['totalNeg'] = data_city.get('totalNeg', 0) + 1
data_city.setdefault('totalPos', 0)
counts[city] = data_city
return counts
def preprocessTweets(data):
processed = []
for i in data:
tmp = []
tmp.append(i[0].decode("utf-8"))
tmp.append(i[1])
tmp.append(len(i[0]))
tmp.append(i[2])
tmp.append(i[-1])
tmp.append(sentiment_score(i[0].decode("utf-8")))
processed.append(tmp)
return processed
def preprocessTweets(data):
processed = []
for i in data:
tmp = []
tmp.append(i[0].decode("utf-8"))
tmp.append(i[1])
tmp.append(len(i[0]))
tmp.append(i[2])
tmp.append(i[-1])
tmp.append(sentiment_score(i[0].decode("utf-8")))
processed.append(tmp)
return processed
def post(self):
tweet = self.get_argument("tweet", default="")
hashtag = self.get_argument("hashtag", default="")
t = tornado.template.Template(html)
if tweet:
score = sentiment_score(tweet)
self.write(t.generate(tweet_senti=str(score), hashtag_senti="0"))
elif hashtag:
tweets = api.search(hashtag, count=100)
tweets = [tweet.text for tweet in tweets]
scores = sentiment_scores_of_sents(tweets)
for score, tweet in zip(scores, tweets):
print score, tweet.encode('utf8')
mean_score = np.mean(scores)
self.write(t.generate(tweet_senti="0", hashtag_senti=str(mean_score)))
else:
self.write(t.generate(tweet_senti="0", hashtag_senti="0"))
def post(self):
tweet = self.get_argument("tweet", default="")
hashtag = self.get_argument("hashtag", default="")
t = tornado.template.Template(html)
if tweet:
score = sentiment_score(tweet)
self.write(t.generate(tweet_senti=str(score), hashtag_senti="0"))
elif hashtag:
tweets = api.search(hashtag, count=100)
tweets = [tweet.text for tweet in tweets]
scores = sentiment_scores_of_sents(tweets)
for score, tweet in zip(scores, tweets):
print score, tweet.encode('utf8')
mean_score = np.mean(scores)
self.write(
t.generate(tweet_senti="0", hashtag_senti=str(mean_score)))
else:
self.write(t.generate(tweet_senti="0", hashtag_senti="0"))
from sentiment import sentiment_score
__author__ = 'ravi'
print( sentiment_score("go die"))
#Calculate individual and average sentiment score
#Individual sentiment score
from sentiment import sentiment_score
import json
data = []
for line in open('z_test_data.txt'):
b = line
a = sentiment_score(b)
data.append([b,a])
with open('z_saved_data.txt', 'w') as outfile:
json.dump(data, outfile)
def on_status(self, status):
""" This method is called whenever new data arrives from the live stream.
We asynchronously push this data to Kafka queue.
"""
# msg = status.text.encode('utf-8')
try:
twitter_json = json.dumps(status._json).encode('utf-8')
twitterFilterJSON = json.loads(twitter_json)
if twitterFilterJSON["entities"][
'user_mentions'] is not None and len(
twitterFilterJSON["entities"]['user_mentions']) > 0:
if twitterFilterJSON['place'] is not None:
if not twitterFilterJSON['text'].startswith('RT'):
toUser_id = twitterFilterJSON["entities"][
'user_mentions'][0]["id_str"]
twitter_id_json = json.dumps(
api.get_user(toUser_id)._json).encode('utf-8')
toUserId_filter_JSON = json.loads(twitter_id_json)
# print toUserId_filter_JSON
if toUserId_filter_JSON[
"profile_location"] is not None:
# print (twitterFilterJSON['text'])
# print "TO:" + toUserId_filter_JSON["profile_location"]['name']
# print "From:" + str(twitterFilterJSON['place']['full_name'])
toLocationCoordinates = self.geolocator.geocode(
toUserId_filter_JSON["profile_location"]
['name'].encode('utf-8'))
fromLocationCoordinates = self.geolocator.geocode(
twitterFilterJSON['place']['full_name'].encode(
'utf-8'))
if twitterFilterJSON["lang"].startswith("en"):
sentscore = sentiment_score(
twitterFilterJSON['text'].encode('utf-8'))
if sentscore > 0.5:
sentiment = 'Pos'
else:
sentiment = 'Neg'
else:
sentiment = 'Pos'
#sentiment = "Pos"#subprocess.check_output('python', '~/nn/twitter-sentiment-cnn', '--load', 'run20170423-124859', '--custom_input', twitterFilterJSON['text'].encode('utf-8'))
if toLocationCoordinates != None and fromLocationCoordinates != None:
tweetJSON = "{\"text\": \"" + twitterFilterJSON['text'].encode(
'utf-8'
).strip(
'\\'
) + "\", \"language\": \"" + twitterFilterJSON[
'lang'].encode(
'utf-8'
) + "\", \"sentiment\": \"" + sentiment.encode(
'utf-8'
) + "\", \"fromLocation\": \"" + twitterFilterJSON[
'place']['full_name'].encode(
'utf-8'
) + "\", \"fromLocationLat\": \"" + str(
fromLocationCoordinates.latitude
) + "\", \"fromLocationLong\": \"" + str(
fromLocationCoordinates.longitude
) + "\", \"toLocation\": \"" + toUserId_filter_JSON[
"profile_location"]['name'].encode(
'utf-8'
) + "\", \"toLocationLat\": \"" + str(
toLocationCoordinates.latitude
) + "\", \"toLocationLong\": \"" + str(
toLocationCoordinates.longitude
) + "\" }".encode('utf-8')
print tweetJSON
self.producer.send_messages(
'twitterstream', tweetJSON)
except Exception as e:
# Catch any unicode errors while printing to console
# and just ignore them to avoid breaking application.
pass
print(e)
return False
return True
file1 = open(OUTPUT_FOLDER_NAME + '/' + keyword['word'] + '.output', 'r')
lines = file1.readlines()
sttime = json.loads(lines[0])['created_at']
#print(keyword['word'])
all_likes = 0
all_retweets = 0
score = 0
num = 0
for line in lines:
data = json.loads(line)
msg = data['tweet']
likes = data['likes_count']
retweets = data['retweets_count']
like_score = likes * 0.5
rewtweet_score = retweets * 1.0
sentiment = sentiment_score(msg)
if (sentiment < 0.3 or sentiment > 0.7):
if multiplier == -1:
sentiment = 1 - sentiment
score += sentiment + like_score + rewtweet_score
num += abs((1 + like_score + rewtweet_score))
#Save featured tweets
if len(msg) <= featured_char_limit and (not ("@" in msg)) and (not ("https://" in msg)):
featured.append(data)
all_likes += likes
all_retweets += retweets
score = score / num
averages.append(score)
# print("likes: ", all_likes, ", retweets", all_retweets)
df = pd.read_csv(item_path)
item_columns = df.columns
for review in review_list:
if review in item_columns:
str_list = df[review]
# print str_list
score_list = []
count = 0
total_count = len(str_list)
print total_count
tqdm_iter = tqdm(enumerate(list(str_list)))
for i in tqdm_iter:
try:
str_item = unicode(i[1].encode("utf-8"))
count += 1
str_item_score_tmp = sentiment_score(str_item)
score_list.append(str_item_score_tmp)
except:
log_error(f, i[0], str(i[1]))
count += 1
score_list.append(str(i[1]))
score_list = pd.DataFrame(score_list)
df = pd.concat([df, score_list], axis=1)
ori_columns = list(df.columns)
ori_columns[-1] = review + u'_score'
df.columns = pd.Series(ori_columns)
save_path = result_path + item
df.to_csv(save_path)
print df
def main():
#message = "happiness is what"
#print (sentiment_score(message))
print(sentiment_score(u"I love you"))
def delete_document(self, dbs_name, document_id, rev):
url = 'http://{ip_address}:5984/{dbs_name}/{document_id}?rev={rev}'.format(
ip_address=self.ip_address,
dbs_name=dbs_name,
document_id=document_id,
rev=rev)
response = requests.delete(url, auth=self.auth)
return response.json()
if __name__ == '__main__':
my_couchdb = couchDb_utils('admin', 'password', 'localhost')
# res = my_couchdb.insert_document('demo', {'_id': 'second_record', 'init_balance': 1500})
# res = my_couchdb.get_document('demo', 'second_record')
# res = my_couchdb.delete_document('demo', 'second_record', "1-9528dce32655253d363029732a718a23")
# print res
with open('tinyTwitter.json', 'r') as f:
tiny_twitter = json.loads(f.read())
twitters = tiny_twitter['rows']
for twitter in twitters:
text = twitter['doc']['text']
matched = re.findall(r'https?://t\.co/.{10}', text, re.MULTILINE)
if matched:
for strip_str in matched:
text = text.replace(strip_str, '')
score = sentiment_score(text)
data = {'positiveness': score, 'tweet': text}
my_couchdb.insert_document('demo', data)