I recently published a post titled, “4 Key Tweeting Attributes of Guy Kawasaki in one Infographic.” I made extensive use of SAS to gather and manipulate the data from Twitter. Turns out, SAS is pretty awesome for this type of work. In this post I’m going to document how to use SAS to gather data from Twitter’s API. My next post on SAS and Twitter will build off of this one and teach you how to gather data about your subject’s followers, find ReTweets, and listen in on conversations. Click here to get that post delivered to your inbox as soon as it’s published.
First off, you might wonder, why do this? Well, successful analyzers of the future will be adept at analyzing all sorts of data, including data from social networks, like Twitter. Also, if you’re looking to market your analytical skills, what hiring manager wouldn’t be impressed with someone who gathered data from Twitter’s API with SAS, then mined, analyzed, and presented the data in a compelling way. Oh, almost forgot, because you’re analyzing a current event (it’s on Twitter, right?) and mentioning Twitter in your post, your analysis will be more search engine friendly, so you’ll likely get a wider and more targeted audience than if you analyzed something outside of the Twitterverse. Some smart analyzers have even been known to analyze Tweets about their target employer and use the analysis to help get themselves hired. On a larger scale, this is almost exactly what Seth Godin has done with Brands in Public.
Before we get started I have to tell you a little about Twitter’s rate limiting policy. Unfortunately, the search area of Twitter’s API doesn’t have a hard rate limit. Rather, Twitter says they allow a rate limit quite a bit higher than their standard 150 hits/hour, but they decline to say how much. Full documentation can be found here, about 1/2 down the page. I have run afoul of the limit before and guess that it’s around 600 hits per hour or more than 30 per minute. When you exceed the unpublished rate, you have to wait between 1-3 hours for your ip address to be allowed to his Twitter again. If you’re just searching for someone’s post, like we’re doing with Guy Kawasaki, you needn’t worry about getting anywhere near Twitter’s rate limit.
Ok, so now let’s get started.
After you figure out what you want to search for (this site is a good start to find trends, and they graph them out for you), you’ll need to plug your search term into the url string that your SAS program will use. If you’re searching for a person, like I did, your string will look like this:
The ‘q=from’ tells Twitter that you’re searching for Tweets from a specific user. The ‘%3A’ is url encoding for a ‘:’. And the ‘&rpp’ tells Twitter to return the maximum (100) items per page. You can copy and paste that string into your browser right now and get back some nicely formatted xml representing Guy’s last 100 Tweets.
Step 2: Ok, you know what you’re searching for and how to format the url string to get your results. But Twitter returns a paltry 100 results at a time. You’re a SAS user, you don’t work with 100 record data sets! You want more, so you wrap your code in a macro, key off of Twitter’s page= parameter to get older results, and append the new results to your master dataset. Twitter will generally allow you to pull down 1 week’s worth of search results. The code to do this is located here.
That’s enough to get you started. You now have a SAS data set with lots of Twitter data, including text to mine, dates and times to trend out, and, hopefully, an interesting topic to help show showcase your analytical prowess to your audience.
You can access the full code here.
Don’t forget to come back in about 2 weeks to read my post on how to wrangle and append other data from Twitter to your search dataset. Or, better yet, click here and get all of my posts in your inbox as soon as they’re published.