The easiest way to handle an HTTP streaming in Python is to use PyCurl, the Python bindings for the famous Curl network library. PyCurl allows you to provide a callback function that will be executed every time a new block of data is available. The following code is a simple demonstration of how we can use HTTP streaming with PyCurl in order to analyzie a stream of tweet:
from __future__ import division
from collections import defaultdict
from pylab import barh,show,yticks
import pycurl
import simplejson
import sys
import nltk
import re
def plot_histogram(freq, mean):
# using dict comprehensions to remove not frequent words
topwords = {word : count
for word,count in freq.items()
if count > round(2*mean)}
# plotting
y = topwords.values()
x = range(len(y))
labels = topwords.keys()
barh(x,y,align='center')
yticks(x,labels)
show()
class TwitterAnalyzer:
def __init__(self):
self.freq = defaultdict(int)
self.cnt = 0
self.mean = 0.0
# composing the twitter stream url
nyc_area = 'locations=-74,40,-73,41'
self.url = "https://stream.twitter.com/1.1/statuses/filter.json?"+nyc_area
def begin(self,usr,pws):
"""
init and start the connection with twitter using pycurl
usr and pws must be valid twitter credentials
"""
self.conn = pycurl.Curl()
# we use the basic authentication,
# in future oauth2 could be required
self.conn.setopt(pycurl.USERPWD, "%s:%s" % (usr, pws))
self.conn.setopt(pycurl.URL, self.url)
self.conn.setopt(pycurl.WRITEFUNCTION, self.on_receive)
self.conn.perform()
def on_receive(self,data):
""" Handles the arrive of a single tweet """
self.cnt += 1
tweet = simplejson.loads(data)
# a little bit of natural language processing
tokens = nltk.word_tokenize(tweet['text']) # tokenize
tagged_sent = nltk.pos_tag(tokens) # Part Of Speech tagging
for word,tag in tagged_sent:
# filter sigle chars words and symbols
if len(word) > 1 and re.match('[A-Za-z0-9 ]+',word):
# consider only adjectives and nouns
if tag == 'JJ' or tag == 'NN':
self.freq[word] += 1 # keep the count
# print the statistics every 50 tweets
if self.cnt % 50 == 0:
self.print_stats()
def print_stats(self):
maxc = 0
sumc = 0
for word,count in self.freq.items():
if maxc < count:
maxc = count
sumc += count
self.mean = sumc/len(self.freq)
print '-------------------------------'
print ' tweets analyzed:', self.cnt
print ' words extracted:', len(self.freq)
print ' max frequency:', maxc
print ' mean frequency:', self.mean
def close_and_plot(self,signal,frame):
print ' Plotting...'
plot_histogram(self.freq,self.mean)
sys.exit(0)
In the constructor of this class we initialize a dictionary that will contain the frequency of each word, a string that contains the url of the service we need to call (composed in order to query twitter for the tweets in NYC) and the variables cnt and mean to keep track of the number of tweets analyzed and of the mean frequency over all the words.In the method begin, we use the PyCurl library for the authentication to Twitter and start the connection. In particular, we set that the method on_receive is the callback function demanded to processing of the incoming. In this method the actual analysis is done, every tweet is split in tokens and a part of speech tagging is performed. Then, the frequency of all the words that are adjective or nouns is updated.
The method print_stats is used to print the our statistics on the console while close_and_plot plots an histogram using the frequencies in the dictionary and closes the program.
Let's use this class:
import signal usr = 'supersexytwitteruser' pws = "yessosexyiam" ta = TwitterAnalyzer() # invoke the close_and_plot() method when a Ctrl-D arrives signal.signal(signal.SIGINT, ta.close_and_plot) ta.begin(usr,pws) # run the analysisIn the code above, a TwitterAnalyzer object is instantiated, its method close_and_plot is registered as handler for the SIGINT signal and, finally, begin is invoked.
This code starts a program which analyzes all the tweet of the New York Area in real time and prints the statistics every 50 tweets, just like follows:
------------------------------- tweets analyzed: 50 words extracted: 110 max frequency: 8 mean frequency: 1.12727272727 ------------------------------- tweets analyzed: 100 words extracted: 200 max frequency: 22 mean frequency: 1.235 ------------------------------- tweets analyzed: 150 words extracted: 286 max frequency: 29 mean frequency: 1.26573426573 ------------------------------- tweets analyzed: 200 words extracted: 407 max frequency: 39 mean frequency: 1.31203931204 ------------------------------- tweets analyzed: 250 words extracted: 495 max frequency: 49 mean frequency: 1.37575757576Pressing Cntrl-D we can stop the program and plot a bar chart of adjectives and nouns detected. This is what I got in the morning of March 21, 2013:
We see that is very common to post a link in a tweet (turned out that http is considered as a noun most of the time) and that the words day, today, good and morning were the most used during the analysis.
