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%load_ext watermark
%watermark -a 'Sebastian Raschka' -v -d
Before we get started, I want to list some of the required packages to make this work!
Below, you find a list of the basic packages which can be installed via
pip install <package_name>
And the Python (2.7) wordcloud package by Andreas Mueller can be installed via
pip install git+git://github.com/amueller/word_cloud.git
Note that wordcloud requires Python's imaging library PIL. Depending on the operating system, the installation and setup of PIL can be quite challenging; however, when I tried to install it on different MacOS and Linux systems via conda it always seemed to work seamlessly:
conda install pilLet me use my handy watermark extension to summarize the different packages and version numbers that were used in my case to download the twitter timeline and create the word cloud:
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%load_ext watermark
%watermark -d -v -m -p twitter,pyprind,wordcloud,pandas,scipy,matplotlib
In order to get access to the Twitter API through OAuth (open standard for authorization), we have to obtain our consumer information and access tokens first by registering our app on https://apps.twitter.com.
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CONSUMER_KEY = 'enter your information here'
CONSUMER_SECRET = 'enter your information here'
ACCESS_TOKEN = 'enter your information here'
ACCESS_TOKEN_SECRET = 'enter your information here'
USER_NAME = 'enter your twitter handle here'
The following code can then be used to download your timeline "sequentially" in chunks of 200 tweets per request to overcome the twitter API limitation, which only allows you to download 200 tweets at a time.
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# Sebastian Raschka, 2014
# Code for downloading your personal twitter timeline.
import twitter
from datetime import datetime
import time
import re
import sys
import pandas as pd
import pyprind as pp
class TimelineMiner(object):
def __init__(self, access_token, access_secret, consumer_key, consumer_secret, user_name):
self.access_token = access_token
self.access_secret = access_secret
self.consumer_key = consumer_key
self.consumer_secret = consumer_secret
self.user_name = user_name
self.auth = None
self.df = pd.DataFrame(columns=['timestamp', 'tweet'], dtype='str')
def authenticate(self):
self.auth = twitter.Twitter(auth=twitter.OAuth(self.access_token,
self.access_secret, self.consumer_key,
self.consumer_secret))
return bool(isinstance(self.auth, twitter.api.Twitter))
def get_timeline(self, max=0, keywords=[]):
if keywords:
self.df['keywords'] = ''
tweet_ids = [self.auth.statuses.user_timeline(
user_id=self.user_name, count=1
)[0]['id']] # the ID of my last tweet
last_count = 200
counter = 0
while last_count == 200:
timeline = self.auth.statuses.user_timeline(user_id=self.user_name, count=200, max_id=tweet_ids[-1])
for tweet in range(len(timeline)):
text = timeline[tweet]['text'].replace('"', '\'')
tweet_id = int(timeline[tweet]['id'])
date = self.__get_date(timeline, tweet)
if keywords:
for k in keywords:
if self.__check_keyword(text,k):
self.df.loc[counter,'tweet'] = text
self.df.loc[counter,'timestamp'] = date
try:
self.df.loc[counter,'keywords'].append(k)
except AttributeError:
self.df.loc[counter,'keywords'] = [k]
try:
self.df.loc[counter,'keywords'] = ';'.join(self.df.loc[counter,'keywords'])
except KeyError:
pass
else:
self.df.loc[counter,'tweet'] = text
self.df.loc[counter,'timestamp'] = date
counter += 1
if max and counter >= max:
break
sys.stdout.flush()
sys.stdout.write('\rTweets downloaded: %s' %counter)
if max and counter >= max:
break
last_count = len(timeline)
tweet_ids.append(timeline[-1]['id'])
time.sleep(1)
print()
def make_csv(self, path):
self.df.to_csv(path, encoding='utf8')
def __get_date(self, timeline, tweet):
timest = datetime.strptime(timeline[tweet]['created_at'],
"%a %b %d %H:%M:%S +0000 %Y")
date = timest.strftime("%Y-%d-%m %H:%M:%S")
return date
def __check_keyword(self, s, key):
return bool(re.search(key, s, re.IGNORECASE))
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tm = twitter_timeline.TimelineMiner(ACCESS_TOKEN,
ACCESS_TOKEN_SECRET,
CONSUMER_KEY,
CONSUMER_SECRET,
USER_NAME)
print('Authentification successful: %s' %tm.authenticate())
tm.get_timeline(max=2000, keywords=[])
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tm.df.head()
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If the twitter_timeline.py script was executed terminal, you can read the "tweets" from the CSV file via
import pandas as pd
df = pd.read_csv('path/to/CSV')
Now that we collected the tweets from our twitter timeline the creation of the word cloud is pretty simple and straightforward thanks to the nice wordcloud module.
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%matplotlib inline
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import matplotlib.pyplot as plt
from wordcloud import WordCloud, STOPWORDS
# join tweets to a single string
words = ' '.join(tm.df['tweet'])
# remove URLs, RTs, and twitter handles
no_urls_no_tags = " ".join([word for word in words.split()
if 'http' not in word
and not word.startswith('@')
and word != 'RT'
])
wordcloud = WordCloud(
font_path='/Users/sebastian/Library/Fonts/CabinSketch-Bold.ttf',
stopwords=STOPWORDS,
background_color='black',
width=1800,
height=1400
).generate(no_urls_no_tags)
plt.imshow(wordcloud)
plt.axis('off')
plt.savefig('./my_twitter_wordcloud_1.png', dpi=300)
plt.show()
Surprise, surprise: The most common term I used in my tweets is obviously "Python!"
To make the word cloud even more visually appealing, let us as a custom shape in form of the twitter logo:
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from scipy.misc import imread
twitter_mask = imread('./twitter_mask.png', flatten=True)
wordcloud = WordCloud(
font_path='/Users/sebastian/Library/Fonts/CabinSketch-Bold.ttf',
stopwords=STOPWORDS,
background_color='white',
width=1800,
height=1400,
mask=twitter_mask
).generate(no_urls_no_tags)
plt.imshow(wordcloud)
plt.axis("off")
plt.savefig('./my_twitter_wordcloud_2.png', dpi=300)
plt.show()
(You can find the twitter_mask.png here)
