![]() Write down the words/phrases that you do not wish to appear in the word cloud (for examples, see below), then proceed to the next step. Rather than look at the word cloud itself, inspection is best performed by scrolling through the “Relevant Words” box, on the right side of the page. Before scrutinizing, be sure to select (in the prior step) the number of words/phrases to include in the word cloud. Inspect the word cloud to ensure all of the included words/phrases are desired. You will be brought to a page that looks like the following screenshot. With the paper prepared for analysis, now you can create a word cloud for it. This involves deleting references, tables, charts, acknowledgements, and other text that can unduly shape the word cloud. To prepare the paper for MonkeyLearn, you should: InstructionsĪs a starting point, we assume you are looking at the paper for which you will create a word cloud. For our purposes, we concluded that MonkeyLearn is best because it, unlike the others, uses artificial intelligence (AI) to capture phrases, not only words offers more ways to format the word cloud and, requires less deletion of uninformative words (e.g., “and,” “to”). ![]() To ensure equal access, we only experimented with the open choices. #HOW TO MAKE WORD CLOUD IN NVIVO FREE#Free options are MonkeyLearn, TagCrowd, and WordItOut. You can use popular but expensive qualitative data analysis programs, like NVivo and ATLAS.ti. To get you started on making word clouds, we walk you through what is involved. Here is our process: The author(s) send us the final version of their paper Josh makes a word cloud for it, using the steps described below he sends it to Scott, who reviews the word cloud and asks for clarification or changes, as needed then we send it to the author(s), asking them to do the same finally, we insert the word cloud into the article’s dedicated page, between the abstract and introduction. ![]() Because the latter can be reliably created, the editorial team will do so on the authors’ behalf, but gain their approval prior to publication. In articles published by the current editorial team (i.e., those after volume 9, issue 1), abstracts will be followed by word clouds. By contrast, consider what you learn about the article from looking at its word cloud: They were listed as “police,” “motivation,” “autonomy,” and “boredom.” They tell you little about the article even less if you already know its title. So you can see the effect, let us take a look at Phillips’ (2017) “Self-Motivation in Policing.” 4 This is the first article in QC to include keywords. Now, all QC articles are accompanied by a word cloud (on a particular page, described in the next subsection). Plus, they are more interesting to look at and, thus, garner more interest from readers. Compared to keywords, word clouds offer more information not only because they include more words, but also because their importance is reflected in their size. That got us thinking: Why not replace keywords with word clouds? 3 Because of how they are created, they are valid and reliable representations of articles’ text. We asked about keywords because they are a convention, 2 not because they are useful. Even PMC, believe it or not, doesn’t appear to index keywords. We don’t support keywords because, honestly, we looked around at how many aggregators actually use them and found few actually look at them in returning results. We noticed that PubPub does not have a field for entering them, so we asked the platform’s Head of Operations, Gabe Stein, about it. While moving QC from its old to new website, a question about keywords came up. They are a great way to capture people’s attention and get them reading. #HOW TO MAKE WORD CLOUD IN NVIVO HOW TO#Below, we explain the decision and show you how to make word clouds for your papers. We are the first criminology journal, at least, to take this step. Therefore, …Qualitative…Criminology ( QC) is replacing keywords with word clouds. 1 For understanding articles at a glance, they are far superior to keywords. ![]() In this editorial, we introduce a third option: word clouds. Abstracts and keywords are commonly used to summarize articles. ![]()
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