Word cloud: Your most used words on LinkedIn
Keep your content aligned with your LinkedIn goals, get a visual summary of your most used words across posts.
Written by Andreas Jonsson. Updated over a week ago
Align your LinkedIn goals with your content
Let’s say you want to be known on LinkedIn for «marketing, empathy, and leadership». How do you keep track that your content reflects this?
With the new feature «Word Cloud» you can now stay focused on topics that matter the most to you and your audience. It’s also a great way to see how your content has evolved over time.
What is it?
It’s a snapshot of the most used words in your LinkedIn content.
Where is it?
In the Report section, scroll down.
What to look for?
Look for the words in the biggest size and colors. They represent your most used words .
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Variety and novelty in English lessons encourage learners to be more engaged. On the contrary, monotonous tasks, no matter how useful they are, cause boredom. How to bring variety without too many changes in the process of learning vocabulary? Use word clouds.
Word clouds are a graphical representation of words and words combinations. They can be used for:
- vocabulary revision
- presenting lexis
- practicing new words
- student’s projects.
It is just a more visually appealing way to teach vocabulary. Instead of using lists, which students are probably fed up with, make clouds. You can utilize them for word searches, making monologues and dialogues, word games, writing tasks, etc.
Here are some examples of the tasks.
Task 1
Make a story about a detective investigating a case using all the words and tell your partner (the teacher).
Task 2
Work in pairs. Ask and answer five questions using the words.
Task 3
Give a definition of one of the words. Can your partner guess the word? Swap roles.
There are some services for creating word clouds:
Word Art
Wordle
Word clouds
I personally prefer to use Word art because it’s possible to create clouds containing collocations. As we know teaching collocations is more effective for learners. Other services just break the word pairs into single words.
Let’s see how to create word clouds using Word art. It’s quick and easy, no registration is required. So click ‘Create’ and make your cloud.
Step 1
Fill in the words or word combinations. You can type as many words as you need, even 30 or even 50. It’s possible to Capitalize letters, use the UPPER or lower case. Click ‘Options’ and opt for repetition of the words as you see in one of the pictures above or choose no repetition.
Step 2
Choose the shape of your cloud: animals, nature, people or some holidays themes. If you want to see the changes, click the red button ‘Visualize’.
For example, this charming ladybird will definitely catch kids’ interest.
Step 3
Choose a font which you like. There are more than 50 fonts to select.
Step 4
Pick out a layout: horizontal, vertical, crossing words, dancing words, slopes and random. The layout will depend on the task and the learners. Teens prefer something less ordinal and more creative. For adults, I usually make more conservative things like a horizontal layout.
Step 5
Choose a style: colours of the words and the background. If you want to change a colour of the words, click ‘Words colours’ and ‘Custom’ and add more colours to the pallette.
Step 6
To apply all the changes, press ‘Print’ or ‘Download’ your cloud and enjoy it in your lessons.
Text mining methods allow us to highlight the most frequently used keywords in a paragraph of texts. One can create a word cloud, also referred as text cloud or tag cloud, which is a visual representation of text data.
The procedure of creating word clouds is very simple in R if you know the different steps to execute. The text mining package (tm) and the word cloud generator package (wordcloud) are available in R for helping us to analyze texts and to quickly visualize the keywords as a word cloud.
In this article, we’ll describe, step by step, how to generate word clouds using the R software.
word cloud and text mining, I have a dream speech from Martin luther king
Contents
- 3 reasons you should use word clouds to present your text data
- Who is using word clouds ?
- The 5 main steps to create word clouds in R
- Step 1: Create a text file
- Step 2 : Install and load the required packages
- Step 3 : Text mining
- Step 4 : Build a term-document matrix
- Step 5 : Generate the Word cloud
- Go further
- Explore frequent terms and their associations
- The frequency table of words
- Plot word frequencies
- Infos
3 reasons you should use word clouds to present your text data
- Word clouds add simplicity and clarity. The most used keywords stand out better in a word cloud
- Word clouds are a potent communication tool. They are easy to understand, to be shared and are impactful
- Word clouds are visually engaging than a table data
Who is using word clouds ?
- Researchers : for reporting qualitative data
- Marketers : for highlighting the needs and pain points of customers
- Educators : to support essential issues
- Politicians and journalists
- social media sites : to collect, analyze and share user sentiments
The 5 main steps to create word clouds in R
Step 1: Create a text file
In the following examples, I’ll process the “I have a dream speech” from “Martin Luther King” but you can use any text you want :
- Copy and paste the text in a plain text file (e.g : ml.txt)
- Save the file
Note that, the text should be saved in a plain text (.txt) file format using your favorite text editor.
Step 2 : Install and load the required packages
Type the R code below, to install and load the required packages:
# Install
install.packages("tm") # for text mining
install.packages("SnowballC") # for text stemming
install.packages("wordcloud") # word-cloud generator
install.packages("RColorBrewer") # color palettes
# Load
library("tm")
library("SnowballC")
library("wordcloud")
library("RColorBrewer")Step 3 : Text mining
load the text
The text is loaded using Corpus() function from text mining ™ package. Corpus is a list of a document (in our case, we only have one document).
- We start by importing the text file created in Step 1
To import the file saved locally in your computer, type the following R code. You will be asked to choose the text file interactively.
text <- readLines(file.choose())In the example below, I’ll load a .txt file hosted on STHDA website:
# Read the text file from internet
filePath <- "http://www.sthda.com/sthda/RDoc/example-files/martin-luther-king-i-have-a-dream-speech.txt"
text <- readLines(filePath)- Load the data as a corpus
# Load the data as a corpus
docs <- Corpus(VectorSource(text))VectorSource() function creates a corpus of character vectors
- Inspect the content of the document
inspect(docs)Text transformation
Transformation is performed using tm_map() function to replace, for example, special characters from the text.
Replacing “/”, “@” and “|” with space:
toSpace <- content_transformer(function (x , pattern ) gsub(pattern, " ", x))
docs <- tm_map(docs, toSpace, "/")
docs <- tm_map(docs, toSpace, "@")
docs <- tm_map(docs, toSpace, "\|")Cleaning the text
the tm_map() function is used to remove unnecessary white space, to convert the text to lower case, to remove common stopwords like ‘the’, “we”.
The information value of ‘stopwords’ is near zero due to the fact that they are so common in a language. Removing this kind of words is useful before further analyses. For ‘stopwords’, supported languages are danish, dutch, english, finnish, french, german, hungarian, italian, norwegian, portuguese, russian, spanish and swedish. Language names are case sensitive.
I’ll also show you how to make your own list of stopwords to remove from the text.
You could also remove numbers and punctuation with removeNumbers and removePunctuation arguments.
Another important preprocessing step is to make a text stemming which reduces words to their root form. In other words, this process removes suffixes from words to make it simple and to get the common origin. For example, a stemming process reduces the words “moving”, “moved” and “movement” to the root word, “move”.
Note that, text stemming require the package ‘SnowballC’.
The R code below can be used to clean your text :
# Convert the text to lower case
docs <- tm_map(docs, content_transformer(tolower))
# Remove numbers
docs <- tm_map(docs, removeNumbers)
# Remove english common stopwords
docs <- tm_map(docs, removeWords, stopwords("english"))
# Remove your own stop word
# specify your stopwords as a character vector
docs <- tm_map(docs, removeWords, c("blabla1", "blabla2"))
# Remove punctuations
docs <- tm_map(docs, removePunctuation)
# Eliminate extra white spaces
docs <- tm_map(docs, stripWhitespace)
# Text stemming
# docs <- tm_map(docs, stemDocument)Step 4 : Build a term-document matrix
Document matrix is a table containing the frequency of the words. Column names are words and row names are documents. The function TermDocumentMatrix() from text mining package can be used as follow :
dtm <- TermDocumentMatrix(docs)
m <- as.matrix(dtm)
v <- sort(rowSums(m),decreasing=TRUE)
d <- data.frame(word = names(v),freq=v)
head(d, 10) word freq
will will 17
freedom freedom 13
ring ring 12
day day 11
dream dream 11
let let 11
every every 9
able able 8
one one 8
together together 7Step 5 : Generate the Word cloud
The importance of words can be illustrated as a word cloud as follow :
set.seed(1234)
wordcloud(words = d$word, freq = d$freq, min.freq = 1,
max.words=200, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"))
word cloud and text mining, I have a dream speech from Martin Luther King
The above word cloud clearly shows that “Will”, “freedom”, “dream”, “day” and “together” are the five most important words in the “I have a dream speech” from Martin Luther King.
Arguments of the word cloud generator function :
- words : the words to be plotted
- freq : their frequencies
- min.freq : words with frequency below min.freq will not be plotted
- max.words : maximum number of words to be plotted
- random.order : plot words in random order. If false, they will be plotted in decreasing frequency
- rot.per : proportion words with 90 degree rotation (vertical text)
- colors : color words from least to most frequent. Use, for example, colors =“black” for single color.
Go further
Explore frequent terms and their associations
You can have a look at the frequent terms in the term-document matrix as follow. In the example below we want to find words that occur at least four times :
findFreqTerms(dtm, lowfreq = 4) [1] "able" "day" "dream" "every" "faith" "free" "freedom" "let" "mountain" "nation"
[11] "one" "ring" "shall" "together" "will" You can analyze the association between frequent terms (i.e., terms which correlate) using findAssocs() function. The R code below identifies which words are associated with “freedom” in I have a dream speech :
findAssocs(dtm, terms = "freedom", corlimit = 0.3)$freedom
let ring mississippi mountainside stone every mountain state
0.89 0.86 0.34 0.34 0.34 0.32 0.32 0.32 The frequency table of words
head(d, 10) word freq
will will 17
freedom freedom 13
ring ring 12
day day 11
dream dream 11
let let 11
every every 9
able able 8
one one 8
together together 7Plot word frequencies
The frequency of the first 10 frequent words are plotted :
barplot(d[1:10,]$freq, las = 2, names.arg = d[1:10,]$word,
col ="lightblue", main ="Most frequent words",
ylab = "Word frequencies")
word cloud and text mining
Infos
This analysis has been performed using R (ver. 3.3.2).
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If you haven’t tried ProWritingAid’s Word Cloud Gallery, get ready to have your mind blown.
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What is a Word Cloud?
Google says a word cloud is “an image composed of words used in a particular text or subject, in which the size of each word indicates its frequency or importance.”
So, the more often a specific word appears in your text, the bigger and bolder it appears in your word cloud.
ProWritingAid has a Word Cloud Gallery that makes it easy to create word clouds based on the text you paste into the tool. Here’s what a word cloud based on the reaping scene in the first chapter of The Hunger Games looks like:
Below are a couple other word clouds. Can you recognise the novels from their word clouds?
Or, if you are up for a challenge, try our Word Cloud Game. Guess 10 classic novels from the word clouds that they generate.
Word Clouds For Fiction Writers
Every writer has words they like to use over and over. When we edit our own words, it’s hard to see what our overused words are. But when we use them too often, our writing sounds redundant. A word cloud can help you identify your overused words. The most commonly used words are the biggest. It’s easy to see if the big words are the words you want to use the most.
Do you have too many dialogue tags? If so, «said» will show up as a larger word. You can then go find places in your writing to add action or emotion beats. Do your characters smirk or shrug all the time? A word cloud can help you find the actions you repeat too often.
A word cloud can also make sure you are focusing on the right characters, themes, and plot points. If your book is about Jane, but Jack’s name is three times bigger, you might need to re-evaluate your manuscript. If your book is about a war, but the words «war» and «battle» are tiny on your word cloud, you’ll need to develop that plot more.
Word Clouds For Copywriters And Bloggers
Search engine optimization. It can be the bane of copywriters and bloggers everywhere. The algorithms are always changing. But it doesn’t have to be a mystery. The most important part of SEO is keywords.
Run your copy or blog post through a word cloud generator. Are your target keywords huge or tiny? What words have you inadvertently turned into SEO keywords? This will help you figure out exactly what edits you need to make to reach those high search engine rankings.
When To Use Word Clouds
For fiction, you can use word clouds of different scenes to compare how your characters feel about the inciting action. A word cloud will help you notice right away if your characters’ reactions are similar in both scenes. You’ll be able to identify where you should have a new reaction or a new emotion.
For business purposes, word clouds can help you find your customers’ pain points. If you collect feedback from your customers, you can generate a word cloud using customers’ language to help identify what is most important to them. Imagine if “long wait time” cropped up as major emphasis words in customer feedback. That should ring a warning bell.
If you are in the business-to-consumer writing industry, a word cloud will highlight overuse of technical jargon. You can make sure your language is accessible to consumers with a quick visual model.
When Not To Use Word Clouds
Simply dumping a scene from your current work in progress into a word cloud generator might show you that you used “said” a lot, but won’t give you the insights you want. But using word clouds to compare your scenes against each other can show you where they’re too similar, use too many of the same unimportant word choices, or simply aren’t consistent between scenes when they need to be.
Likewise, in copywriting for business, you wouldn’t use a word cloud when your content isn’t optimized for keywords. A word cloud on a blog post that’s not SEO enhanced won’t tell you much about your keyword density.
No matter what you’re writing, word clouds are something to use as a last step in your writing process. Word choice falls under «line edits.» If you have an unedited first draft, word clouds won’t help you much. You’ll likely need to make major structural changes before you use a word cloud.
How ProWritingAid’s Word Cloud Gallery Works
At the bottom of every page on ProWritingAid.com is a link under “Resources” called Word Cloud Gallery. When you click on that link, you get something that looks like this:
Notice at the top, you have options to “Create a Word Cloud” or search for the “Latest Word Clouds” by keyword. A sidebar on the right side contains popular tags to help you narrow your focus on already-generated word clouds.
When you click on “Create Word Cloud,” it brings up the following screen:
Simply copy and paste your text into the box or enter your url, and click on “Create Word Cloud.”
Here’s what your new word cloud looks like:
Notice you have an interesting graphical representation of the words in your text, which you can customize by choosing a font, color, and layout. You can also re-create the word cloud with new text, download the image to your computer, or save it to the ProWritingAid gallery so that others can see and use your word cloud.
Final Thoughts
Word clouds are fun to use as a visual aid with blog posts to underscore the keywords on which you’re focusing. Your readers will notice the larger, bold words and understand their importance to your post.
And for fiction writers, word clouds are great to make sure you’re focusing on the right words in your prose. It’s also interesting to see some of the word clouds in the ProWritingAid gallery that other writers have created, especially those in the genre-specific popular tags in the sidebar on the right.
Play our Word Cloud Game here.
How do you see word clouds helping your writing? Let us know if you’re a fiction writer or copywriter and how you might use a word cloud.
Or if you’ve already created a word cloud on ProWritingAid.com, let us know what insights you gained from it.
In the meantime, happy writing!
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