Learning Data Mining with Python

Chapter 6
The code is as follows:
actual_labels = []
label_mapping = dict(tweet_ids)
Next, we are going to create a twitter server to collect all of these tweets.
This is going to take a little longer. Import the twitter library that we used before,
creating an authorization token and using that to create the twitter object:
import twitter
consumer_key = ""
consumer_secret = ""
access_token = ""
access_token_secret = ""
authorization = twitter.OAuth(access_token, access_token_secret,
consumer_key, consumer_secret)
t = twitter.Twitter(auth=authorization)
Iterate over each of the twitter IDs by extracting the IDs into a list using the
following command:
all_ids = [tweet_id for tweet_id, label in tweet_ids]
Then, we open our output file to save the tweets:
with open(tweets_filename, 'a') as output_file:
The Twitter API allows us get 100 tweets at a time. Therefore, we iterate over each
batch of 100 tweets:
for start_index in range(0, len(tweet_ids), 100):
To search by ID, we first create a string that joins all of the IDs (in this batch)
together:
id_string = ",".join(str(i) for i in
all_ids[start_index:start_index+100])
Next, we perform a statuses/lookup API call, which is defined by Twitter.
We pass our list of IDs (which we turned into a string) into the API call in order
to have those tweets returned to us:
search_results = t.statuses.lookup(_id=id_string)
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