Is Blog Content Analysis Really Meaningful?

Corporate blogging is often a great communication strategy for a company, but many companies don’t undertake this method of communication for several reasons.  One is simply that they just don’t know about what to write.  A good way to overcome that roadblock is via content analysis. Yes, it sounds boring, but content analysis can be helpful in creating a dynamite corporate blogging strategy.

The idea is to perform content analysis prior to creating an editorial calendar for your blog content.  In that way you’ll have targeted topics.

But content analysis can be somewhat tedious.  What am I saying?  Content analysis can be very tedious.  In a nutshell, content analysis is the process of counting related words or phrases and then clustering them to identify what the main ideas of the article are.  If the article is long or if there are many articles to review, the appeal of this type of work is similar to counting the holes in the ceiling tiles of your office.  Tedious, yes.  And the sad news is that there really aren’t, at least not yet, any single programs that can do this work in what I would consider to be a reliable fashion.

Now, after we do this wearying work, do we really have much that will help us form content objectives.  What I mean is just imagine how long the work would take us using this methodology.  To have any kind of meaningful sample from this type of exercise, we would need to apply a lot of eyeballs to the task.  No, there must be a better way to conjure up a content strategy.  That better way, IMHO, is conceptual analysis.

Conceptual analysis is explored briefly, but nicely, in an article by John Cass.  In the article John reviews, in general, conceptual analysis as a subset of content analysis.  He puts forward ways to analyze the thoughts and concepts that are present within an article, a group of articles, or even a community of blogs.  His article lays out a “quick and dirty” method of formulating a conceptual analysis.  In his methodology, he quotes eight steps.

1. Decide the level of analysis.
2. Decide how many concepts to code for.
3. Decide whether to code for existence or frequency of a concept.
4. Decide on how you will distinguish among concepts.
5. Develop rules for coding your texts.
6. Decide what to do with “irrelevant” information.
7. Code the texts.
8. Analyze your results.

The process he explores is somewhat academic; indeed he cites these steps from Colorado State University.  You can read his article for detail on each of the above eight steps.  What I want to comment on here is this.  In step 2 he says that it won’t be possible to pre-determine the list of concepts for which you’re hunting.  Yes.  To do so would defeat the objective of the research.  But he does say that by review of the blogs or community of blogs in question, the researcher can identify possible concepts for further study.  Sounds like sentiment analysis.

Sentiment analysis is a prime objective of web monitoring software such as Andiamo, Social Radar, and Radian6.  I’ve used these programs and can recommend them much, much more highly than standard content analysis programs.  For the corporate, rather than academic, user the web monitoring programs will more quickly identify salient concepts or sentiments among the studied texts or blogs or community of blogs.

So as I’ve written in other posts on this site, it’s a matter of the forest or the trees.  The take-away here is to get the big picture and don’t get bogged down in the details.