Finding the social network is only the first step. We want to examine the conversation, also. We'll look at two aspects of this conversion: words and pictures. Our first background mission in this section was to be sure we had Pillow working properly. This will also help us download pictures.
Words are somewhat simpler. Interestingly, the tweet content isn't obvious in the Twitter API definitions. It turns out that "status" is what we're looking for. The resource called statuses/user_timeline has the tweets made by a given user.
Each status or tweet is packaged with a collection of entities. These are the URL references, media attachments, @ user_mentions, # hashtags, and $ symbols. The entities are separated from the body of the tweet, which greatly simplifies our analysis.
Here's a function to get the last 20 tweets from a user:
def tweets_by_screen_name(screen_name):
api = TwitterAPI(consumer_key,
consumer_secret,
...
