How to do basic text-mining

Turn prose into data for insights into your constituents' behaviour. (Photo used via Creative Commons licence. Click photo for source.)

Database users at universities make frequent use of free-text comment fields to store information. Too frequent use, perhaps. Normally, free text is resorted to only when there’s a need to store information of a type that cannot be conveniently coded (preferably from a pre-established “validation table” of allowed values). Unstructured information such as comments requires some work to turn it into data that can reveal patterns and correlations. This work is called text mining.

Here are steps I took to do some rather crude text-mining on a general-comments field in our database. My method was first to determine which words were used most frequently, then select a few common ‘suggestive’ words that might show interesting correlations, and finally to test the variables I made from them for correlations with giving to our institution.

The comments I was trying to get at were generated from our Annual Giving phonathon. Often these comments flag alumni behaviours such as hanging up on the caller, being verbally abusive, or other negative things. As certain behaviours often prompt the same comments over and over (eg. “hung up on the caller”), I thought that certain frequently-occurring keywords might be negatively correlated with giving.

The method outlined below is rather manual. As well, it focuses on single words, rather than word combinations or phrases. There are some fantastic software packages out there for going much deeper, more quickly. But giving this a try is not difficult and will at least give you a taste for the idea behind text mining.

My method was first to discover the most common words that sounded like they might convey some sense of “attitude”:

• Using a query in Access, I extracted the text of all comments, plus comment type, from the database – including the ID of the individual. (We use Banner so this data came from the APACOMT screen.)
• I dumped the data into Excel, and eliminated certain unwanted comments by type code (such as event attendance, bios, media stories, etc.), leaving about 6,600 comments. (I saved this Excel file, to return to later on.)
• I copied only the column of remaining comments, and pasted this text into a basic text editor. (I like to use EditPad Lite, but anything you have that works with big .txt files is fine.)
• I used Find-and-replace to change all spaces into carriage returns, so that each word was on one line.
• I used Find-and-replace again to removed common punctuation (quote marks, periods, commas etc.)
• I changed all uppercase characters to lowercase characters, so “The” wouldn’t be counted separately from “the”.
• The result was a very long column of single words. I copied the whole thing, and pasted it into Data Desk, as a single variable.
• This allowed me to create a frequency table, sorted by count so the most common words would appear at the top. More than 100,000 cases fell into a little less than 5,000 categories (i.e. words).

The most common words were, in order: to, the, a, made, and, be, mail, by, only, from, not, list, removed, nn, of, in, solicitation, he, no, phonathon, she, pledge, is, wishes, said, unhonoured, on, does, was, giving, phone, will, caller, her, donate.

I recognized some of our most common comments, including “made by-mail-only”, “made phonathon no”, “unhonoured pledge”, etc. These states are already covered by specific coding elsewhere in the database, so I skipped over these and looked farther down to some of the more subjective “mood” words, such as “hang” and “hung” (which almost always meant “hung up the phone”), “rude”, “upset”, “never”, “told”, etc.

I went back to my original Excel file of comments and created a few new columns to hold a 0/1 variable for some of these categories. This took some work in Excel, using the “Contains” text filter. So, for example, every comment that contained some variation on the theme of ‘hanging up the phone’ received a 1 in the column called “Hung up”, and all the others got a zero.

From there, it was easy to copy the IDs, with the new variable(s), into Data Desk, where I matched the data up with Lifetime Giving. The idea of course was to discover a new predictor variable or two. For example, it seemed likely that alumni with a 1 for the variable ‘Hung Up’ might have given less than other alumni. As it turned out, though, the individual variables I created on this occasion were not particularly predictive of giving (or of failing to give).

I certainly haven’t given up on the idea, though, because there is much room for improvement in the analysis. For one thing, I was looking for correlations with Lifetime Giving, when I should have specified Phonathon Giving. People who hang up on student callers aren’t non-donors, necessarily; they just don’t care for being contacted by phone. (Why they don’t just ask to be taken off the calling list, I’m not sure.)

In the meantime, this very basic text-mining technique DID prove very useful when I needed to compare two models I had created for our Annual Giving program. I had designed an improved model which specifically targeted phone-receptive alumni, in the hopes of reducing the number of hang-ups and other unpleasant phone encounters. I showed the effectiveness of this approach through the use of text mining, conducted exactly as outlined above. (I’ll detail the results in a future post.)

Do you have a lot of text-based comments in your database? Do you have a lot of text-based response data from (non-anonymous) surveys? Play around with mining that text and see what insights you come up with.