Call me Chris.


You may know I write for a couple of websites. I feel very fortunate to do so. I have been doing some data analysis for the proprietor of one of the platforms. It helped us monitor the trends that the site has experienced. I decided after that experience I wanted to look at my writing and complete a different sort of analysis. That’s what I want to discuss with you today. 

I am in the process of conducting a word cloud analysis on my writing. If you’re not familiar with the term, it provides a graphic of the most common words you use in a particular text. The more a word is used, the larger and bolder that word becomes in the cloud. You can choose to select the top 10, 25, 50, or any number of words from your text to appear in the cloud.

The particular software program I use to complete the qualitative analysis is free to use. I encourage you to play around with it even if you have no desire to dive into any research. The program is called TagCrowd. Follow the link and enjoy. 

Looking at how I’m using the program, I am using it to identify the commonalities in my writing across articles for a particular platform, and across all the media I write for—including this one. I analyzed twenty-two articles today. Uploading them is the easy part. I record every word from each text’s (in my case, articles) cloud and the number of times it was used. I set the parameters to identify the top 50 words as I typically write a minimum of 500-word articles. 

I use Microsoft Excel to record the words and their usage frequency. Over the twenty-two articles I’ve already examined, I have over 800 unique words identified. It is essential to understand that common-usage words, such as articles (the, a, an), conjunctions (and, but, or), and pronouns (he, she, it), are not returned in your cloud search unless you specifically want them. 

I have written thirty separate articles for this particular website. I want to discover how broad of a vocabulary I am using in my writing. I also want to determine which words I am frequently using. Both are valuable metrics for me to use as a dipstick for meeting my personal goals for writing at a high level. 

The website in question is a news/political outlet. Most of the work I do there constitutes my shortest writing. That is not a value-judgment. It is an observation. The website intends to provide stories that interest readers while keeping them moving through more articles. As a result, the ideal length is around 500 words and focuses on news or politics. 

I expect the results from that writing to be slightly different from the other website I work with. The other website I write for wants pieces in the 750 – 2,000 word length, depending on the content. It also addresses news and politics but does so in a more analytical way. I am a fan of both sites and was a paying customer for their content before working with them. I want to disclose that so you don’t get the wrong idea that I’m steering you in any particular direction. 

As you know from reading this blog, I write in a different format here. I expect the word cloud analysis for my blog to have entirely different results. The exciting part for me will be when I compare my writing across all three platforms. The data will inform my future writing. 

I may discover that I am comfortable with the word choice I employ. I may find I need to change my delivery. I’m a big believer that data never lies. The answer will appear when I get through with my analysis. I promise to update this post or write another post (depending on reader interest) about the results. 

I will tell you that I’m already finding some intriguing results. Through twenty-two articles, I was a bit surprised to discover I have mentioned one politician in particular far more than I intended. It’s not that I think it’s ‘good’ or ‘bad’ to write about anyone. I just didn’t set out to write about the person I clearly referenced more than I realized. I shared that with you to illustrate the value of a tool like a word cloud analysis. 

I also intend to use the tool in my fiction. I will plug in my published novel to review the results. I hope I don’t cringe when the data is tabulated. I’m also going to use it on my current project to help edit and prewrite future chapters.

If I bored you to tears by writing about the study of my writing, I sincerely apologize. As a writer, it’s a personal fascination. It probably also comes off as overly-intense for anyone who isn’t a writer. Hopefully, I didn’t drive you away from reading future posts. 

As always, this has been the World According to Chris. Please hit the like button or leave a reply. 

2 thoughts on “Call me Chris.

    1. I finished my review of my 30 articles for the first platform. My top three words were: media, president, trump.

      I wasn’t surprised about the top two. I was surprised to find I wrote more about Trump than I intended. I was also surprised to learn I wrote less about Biden than I thought.

      Like

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