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Chapter 6 Downloading data from a social network We are going to download a corpus of data from Twitter and use it to sort out spam from useful content. Twitter provides a robust API for collecting information from its servers and this API is free for small-scale usage. It is, however, subject to some conditions that you'll need to be aware of if you start using Twitter's data in a commercial setting. First, you'll need to sign up for a Twitter account (which is free). Go to http://twitter.com and register an account if you do not already have one. Next, you'll need to ensure that you only make a certain number of requests per minute. This limit is currently 180 requests per hour. It can be tricky ensuring that you don't breach this limit, so it is highly recommended that you use a library to talk to Twitter's API. You will need a key to access Twitter's data. Go to http://twitter.com and sign in to your account. When you are logged in, go to https://apps.twitter.com/ and click on Create New App. Create a name and description for your app, along with a website address. If you don't have a website to use, insert a placeholder. Leave the Callback URL field blank for this app—we won't need it. Agree to the terms of use (if you do) and click on Create your Twitter application. Keep the resulting website open—you'll need the access keys that are on this page. Next, we need a library to talk to Twitter. There are many options; the one I like is simply called twitter, and is the official Twitter Python library. You can install twitter using pip3 install twitter if you are using pip to install your packages. If you are using another system, check the documentation at https://github.com/sixohsix/ twitter. Create a new IPython Notebook to download the data. We will create several notebooks in this chapter for various different purposes, so it might be a good idea to also create a folder to keep track of them. This first notebook, ch6_get_twitter, is specifically for downloading new Twitter data. [ 107 ]
Social Media Insight Using Naive Bayes First, we import the twitter library and set our authorization tokens. The consumer key, consumer secret will be available on the Keys and Access Tokens tab on your Twitter app's page. To get the access tokens, you'll need to click on the Create my access token button, which is on the same page. Enter the keys into the appropriate places in the following code: import twitter consumer_key = "" consumer_secret = "" access_token = "" access_token_secret = "" authorization = twitter.OAuth(access_token, access_token_secret, consumer_key, consumer_secret) We are going to get our tweets from Twitter's search function. We will create a reader that connects to twitter using our authorization, and then use that reader to perform searches. In the Notebook, we set the filename where the tweets will be stored: import os output_filename = os.path.join(os.path.expanduser("~"), "Data", "twitter", "python_tweets.json") We also need the json library for saving our tweets: import json Next, create an object that can read from Twitter. We create this object with our authorization object that we set up earlier: t = twitter.Twitter(auth=authorization) We then open our output file for writing. We open it for appending—this allows us to rerun the script to obtain more tweets. We then use our Twitter connection to perform a search for the word Python. We only want the statuses that are returned for our dataset. This code takes the tweet, uses the json library to create a string representation using the dumps function, and then writes it to the file. It then creates a blank line under the tweet so that we can easily distinguish where one tweet starts and ends in our file: with open(output_filename, 'a') as output_file: search_results = t.search.tweets(q="python", count=100)['statuses'] for tweet in search_results: if 'text' in tweet: output_file.write(json.dumps(tweet)) output_file.write("\n\n") [ 108 ]
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Learning Data Mining with Python Co
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About the Author Robert Layton has
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Christophe Van Gysel is pursuing a
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www.allitebooks.com
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Table of Contents Preprocessing usi
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Table of Contents Chapter 7: Discov
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Table of Contents GPU optimization
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Preface If you have ever wanted to
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What you need for this book It shou
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Preface Reader feedback Feedback fr
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Getting Started with Data Mining We
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Chapter 1 In the preceding dataset,
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After you have the above "Hello, wo
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Chapter 1 Windows users may need to
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Chapter 1 The dataset we are going
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Chapter 1 As an example, we will co
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Chapter 1 Two rules are near the to
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Chapter 1 The scikit-learn library
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We then iterate over all the sample
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Chapter 1 Overfitting is the proble
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Chapter 1 Summary In this chapter,
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Classifying with scikit-learn Estim
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Predicting Sports Winners with Deci
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Chapter 7 Next, we need to get the
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• method='nelder-mead': This is u
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Beating CAPTCHAs with Neural Networ
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Chapter 8 The red lines indicate th
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Chapter 8 The combination of an app
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Chapter 8 Next we set the font of t
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Chapter 8 We can then extract the s
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Chapter 8 Our targets are integer v
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Chapter 8 From these predictions, w
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Chapter 8 This code correctly predi
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The result is shown in the next gra
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Chapter 8 However, it isn't very go
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Authorship Attribution Attributing
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Authorship Attribution If we cannot
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Authorship Attribution After taking
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Authorship Attribution This dataset
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Authorship Attribution "instead", "
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Authorship Attribution Support vect
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Authorship Attribution Kernels When
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Authorship Attribution We can reuse
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Authorship Attribution With our dat
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Authorship Attribution We then reco
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Authorship Attribution If it doesn'
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Authorship Attribution Finally, we
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Clustering News Articles In most of
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Chapter 10 API Endpoints are the ac
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The token object is just a dictiona
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Chapter 10 We then create a list to
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Chapter 10 We are going to use MD5
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Chapter 10 Next, we develop the cod
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Chapter 10 We use clustering techni
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Chapter 10 We only fit the X matrix
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Chapter 10 We then print out the mo
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Chapter 10 Our function definition
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Chapter 10 The result from the prec
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Chapter 10 Implementation Putting a
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Chapter 10 Neural networks can also
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We then call the partial_fit functi
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Classifying Objects in Images Using
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Chapter 11 This dataset comes from
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You can change the image index to s
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Chapter 11 Each of these issues has
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Chapter 11 Using Theano, we can def
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Chapter 11 Building a neural networ
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Chapter 11 return [image,] return s
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Chapter 11 Getting your code to run
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Chapter 11 Setting up the environme
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This will unzip only one Coval.otf
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Chapter 11 First we create the laye
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Working with Big Data Big data What
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Working with Big Data Governments a
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Working with Big Data We start by c
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Working with Big Data The final ste
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Working with Big Data Getting the d
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Working with Big Data If we aren't
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Working with Big Data Before we sta
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Working with Big Data The first val
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Working with Big Data This gives us
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Working with Big Data Next, we crea
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Working with Big Data Then, make a
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Working with Big Data Left-click th
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Working with Big Data The result is
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Next Steps… Extending the IPython
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Next Steps… Chapter 3: Predicting
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Next Steps… Vowpal Wabbit http://
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Next Steps… Deeper networks These
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Next Steps… Real-time clusterings
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Next Steps… More resources Kaggle
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authorship, attributing 185-188 AWS
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feature extraction about 82 common
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NetworkX about 145 defining 303 URL
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scikit-learn package references 305
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Thank you for buying Learning Data
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Learning Python Data Visualization
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