def create_stream(phrase, queue):
"""
Celery task that connects to the twitter stream and runs a loop, periodically
emitting tweet information to all connected clients.
"""
local = SocketIO(message_queue=queue)
stream = Twitter().stream(phrase, timeout=30)
for i in range(60):
stream.update()
for tweet in reversed(stream):
sentiment = classify_tweet(tweet)
x, y = vectorize_tweet(tweet)
local.emit(
'tweet', {
'id': str(i),
'text': str(tweet.text.encode('ascii', 'ignore')),
'sentiment': sentiment,
'x': x,
'y': y
})
stream.clear()
time.sleep(1)
return queue
def TwitterStream():
# Another way to mine Twitter is to set up a stream.
# A Twitter stream maintains an open connection to Twitter,
# and waits for data to pour in.
# Twitter.search() allows us to look at older tweets,
# Twitter.stream() gives us the most recent tweets.
for trend in Twitter().trends(cached=False):
print trend
# It might take a few seconds to set up the stream.
stream = Twitter().stream("i love", timeout=30)
pos_count=0
neg_count=0
#while True:
for i in range(50):
if(neg_count):
ratio = pos_count / neg_count
else:
ratio = 0
print str(pos_count) + " " + str(neg_count) + " " + str(ratio)+"%"
#print i
#print "+ " + str(pos_count)
#print "- " + str(neg_count)
#print "- - -"
# Poll Twitter to see if there are new tweets.
stream.update()
# The stream is a list of buffered tweets so far,
# with the latest tweet at the end of the list.
for tweet in reversed(stream):
print tweet.text
print tweet.language
sent = pol(tweet.text)
if(sent>0):
pos_count+=1
else:
neg_count+=1
# Clear the buffer every so often.
stream.clear()
# Wait awhile between polls.
time.sleep(1)
print "Final Twitter"
print pos_count
print neg_count
def Pattern_Module_Twitter_Stream():
# Start Stop Watch
t1 = time.time()
# Create a list to Store the Data
List = Twitter().stream('#Fail')
# For 10 Instances
for second in range(10):
# Get Stream Data
value = List.update(bytes=1024)
# Add Value to List if not Empty
if len(value) == 0:
# Pass
continue
else:
# Storing Results
List.append()
# Print Tweet
print('Tweet: %s') % (value.text)
# Get Sentiment
print('Sentiment Analysis of Tweet: %s') % (TextBlob(
str(value.text),
analyzer=NaiveBayesAnalyzer()).sentiment[0].upper())
# Wait 3 Seconds between queries - Do not want to get blocked
time.sleep(3)
return time.time() - t1
def Pattern_Module_Twitter_Stream():
# Start Stop Watch
t1 = time.time()
# Create a list to Store the Data
List = Twitter().stream('#Fail')
# For 10 Instances
for second in range(10):
# Get Stream Data
value = List.update(bytes=1024)
# Add Value to List if not Empty
if len(value) == 0:
# Pass
continue
else:
# Storing Results
List.append()
# Print Tweet
print('Tweet: %s') % (value.text)
# Get Sentiment
print('Sentiment Analysis of Tweet: %s') % (TextBlob(str(value.text),
analyzer=NaiveBayesAnalyzer()).sentiment[0].upper())
# Wait 3 Seconds between queries - Do not want to get blocked
time.sleep(3)
return time.time() - t1
def create_stream(phrase, queue):
local = SocketIO(message_queue=queue)
stream = Twitter().stream(phrase, timeout=120)
for i in range(120):
stream.update()
for tweet in reversed(stream):
sentiment = classify_tweet(tweet)
x, y = vectorize_tweet(tweet)
local.emit(
'tweet', {
'id': str(i),
'text': str(tweet.text.encode('ascii', 'ignore')),
'sentiment': sentiment,
'x': x,
'y': y
})
stream.clear()
time.sleep(1)
return queue
def create_stream(phrase, queue):
"""
Celery task that connects to the twitter stream and runs a loop, periodically
emitting tweet information to all connected clients.
"""
local = SocketIO(message_queue=queue)
stream = Twitter().stream(phrase, timeout=30)
for i in range(60):
stream.update()
for tweet in reversed(stream):
sentiment = classify_tweet(tweet)
x, y = vectorize_tweet(tweet)
local.emit('tweet', {'id': str(i),
'text': str(tweet.text.encode('ascii', 'ignore')),
'sentiment': sentiment,
'x': x,
'y': y})
stream.clear()
time.sleep(1)
return queue
import sys, time
from pattern.web import Twitter
s = Twitter().stream(
'#joy, #happiness, #hopeful, #pleasure, #harmony, #kindness, #affection, #love'
)
for i in range(250):
time.sleep(1)
s.update(bytes=1024)
print s[-1].text if s else ''
import sys
sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", ".."))
import time
from pattern.web import Twitter
# Another way to mine Twitter is to set up a stream.
# A Twitter stream maintains an open connection to Twitter,
# and waits for data to pour in.
# Twitter.search() allows us to look at older tweets,
# Twitter.stream() gives us the most recent tweets.
# It might take a few seconds to set up the stream.
stream = Twitter().stream("I hate", timeout=30)
#while True:
for i in range(10):
print(i)
# Poll Twitter to see if there are new tweets.
stream.update()
# The stream is a list of buffered tweets so far,
# with the latest tweet at the end of the list.
for tweet in reversed(stream):
print(tweet.text)
print(tweet.language)
# Clear the buffer every so often.
stream.clear()
# Wait awhile between polls.
time.sleep(1)
import os, sys; sys.path.insert(0, os.path.join("..", ".."))
import time
from pattern.web import Twitter
# Another way to mine Twitter is to set up a stream.
# A Twitter stream maintains an open connection to Twitter,
# and waits for data to pour in.
# Twitter.search() allows us to look at older tweets,
# Twitter.stream() gives us the most recent tweets.
# It might take a few seconds to set up the stream.
stream = Twitter().stream("I hate", timeout=30)
#while True:
for i in range(100):
print i
# Poll Twitter to see if there are new tweets.
stream.update()
# The stream is a list of buffered tweets so far,
# with the latest tweet at the end of the list.
for tweet in reversed(stream):
print tweet.text
print tweet.language
# Clear the buffer every so often.
stream.clear()
# Wait awhile between polls.
time.sleep(1)
from pattern.web import Twitter
import time
s = Twitter().stream('#snowday')
for i in range(25):
time.sleep(1)
s.update(bytes=1024)
print s[-1].text if s else ''
s.clear()
