With over 1,000,000 words in the English language, why is it that we tend to use the same words over & over? This blog shows a hierarchical approach to help you branch out and choose more descriptive words.
But first, to get you into the mood for a blog about words, here's a picture of my friend Thelma's dictionary. It's from 1929, and belonged to her Grandma Betty - this book certainly has character, eh?!?. How long has it been since you used a paper copy of a dictionary or thesaurus?
And now, on with the blog...
English teacher Kaitlin Robbs came up with a neat tool to help you traverse a hierarchical list of words related to emotional states, to come up with more specific words than just happy, sad, etc. She designed a word wheel, with the more general words at the center, and more specific words radiating towards the outer edges.
Here's a snapshot of her wheel:
This seems like a useful tool, and an interesting layout ... therefore I thought I'd try to create something similar using SAS. I first typed out all the words in a text file, and imported them into a dataset. While I was typing the words, I was suspicious that a few of them might not be spelled correctly, therefore I used SAS' Proc Spell to test them.
proc spell words='emotion_word_wheel_original.txt' verify suggest; run;
This check was quick & simple, and certainly worth the effort. The analysis flagged five words - of the five, the word 'disrespected' is probably OK, but the other four are definitely misspelled in Kaitlin's wheel. Here is the output of Proc Spell:
Next came the fun part - creating the geometry and layout of the custom visualization. I created 3 separate annotate layers for the three rings of the wheels, and overlaid them. I used the annotate pie function to create each pie slice in a data step, and added a text label in the middle of the pie slice (using the cntl2txt function to determine the position for the label). The pie slices are evenly spaced in the outside ring, but the sizes of the inner ring slices vary depending on the number of words hierarchically under them, therefore it's a little trickier to calculate the sizes for those slices. Below are the three individual annotate layers (click each of them if you want to see more detailed images):
And here is the final SAS chart, with the three layers overlaid. Click the image to see the full size interactive version, and then each word will have html hover-text (in case you have trouble reading the angled text), and also drill downs to launch a Google search to look up the definition of each word.
Although the wheel is cool, it occurred to me that some people might have difficulty reading the words at all those angles, so I also created an alternate version using a simple table. Below is a snapshot of the top of the table - click it to see the full table:
So, what's your favorite way to find the perfect word? Feel free to share your tips & tricks in a comment!