wordcloud - Create word cloud chart from text, bag-of-words model, bag-of-n-grams model, or

        LDA model - MATLAB ([original](https://in.mathworks.com/help/textanalytics/ref/ldamodel.wordcloud.html)) ([raw](?raw))

Create word cloud chart from text, bag-of-words model, bag-of-n-grams model, or LDA model

Syntax

Description

Text Analytics Toolbox™ extends the functionality of the wordcloud (MATLAB®) function. It adds support for creating word clouds directly from string arrays, and creating word clouds from bag-of-words models, bag-of-n-gram models, and LDA topics. If you do not have Text Analytics Toolbox installed, then see wordcloud.

wordcloud([str](#mw%5F92837cbd-f24d-4907-94d6-1879639ee216%5Fsep%5Fmw%5Fd42a33eb-d1cc-4d3d-8ae9-39718f947dbb)) creates a word cloud chart by tokenizing and preprocessing the text in str, and then displaying the words with sizes corresponding to the word frequency counts. This syntax supports English, Japanese, German, and Korean text.

example

wordcloud([documents](#d126e61590)) creates a word cloud chart from the words appearing in documents.

example

wordcloud([bag](#d126e61733)) creates a word cloud chart from the bag-of-words or bag-of-n-grams modelbag.

example

wordcloud([tbl](#d126e61606),[wordVar](#d126e61621),[sizeVar](#d126e61647)) creates a word cloud chart from the table tbl. The variables wordVar and sizeVar in the table specify the words and word sizes respectively.

example

wordcloud([words](#d126e61674),[sizeData](#d126e61686)) creates a word cloud chart from elements of words with word sizes specified by sizeData.

wordcloud([C](#d126e61718)) creates a word cloud chart from the elements of categorical array C using frequency counts.

wordcloud([ldaMdl](#d126e61767),[topicIdx](#d126e61790)) creates a word cloud chart from the topic with indextopicIdx of the LDA modelldaMdl.

example

wordcloud(___,[Name,Value](#namevaluepairarguments)) specifies additional WordCloudChart properties using one or more name-value pair arguments.

wordcloud([parent](#d126e61799),___) creates the word cloud in the figure, panel, or tab specified byparent.

[wc](#d126e62405) = wordcloud(___) returns the WordCloudChart object. Use wc to modify properties of the word cloud after creating it. For a list of properties, see WordCloudChart Properties.

Examples

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Extract the text from sonnets.txt using extractFileText and display the text of the first sonnet.

str = extractFileText("sonnets.txt"); extractBefore(str,"II")

ans = "THE SONNETS

 by William Shakespeare
 
 
   I
 
   From fairest creatures we desire increase,
   That thereby beauty's rose might never die,
   But as the riper should by time decease,
   His tender heir might bear his memory:
   But thou, contracted to thine own bright eyes,
   Feed'st thy light's flame with self-substantial fuel,
   Making a famine where abundance lies,
   Thy self thy foe, to thy sweet self too cruel:
   Thou that art now the world's fresh ornament,
   And only herald to the gaudy spring,
   Within thine own bud buriest thy content,
   And tender churl mak'st waste in niggarding:
     Pity the world, or else this glutton be,
     To eat the world's due, by the grave and thee.
 
   "

Display the words from the sonnets in a word cloud.

Figure contains an object of type wordcloud.

Load the example data. The file sonnetsPreprocessed.txt contains preprocessed versions of Shakespeare's sonnets. The file contains one sonnet per line, with words separated by a space. Extract the text from sonnetsPreprocessed.txt, split the text into documents at newline characters, and then tokenize the documents.

filename = "sonnetsPreprocessed.txt"; str = extractFileText(filename); textData = split(str,newline); documents = tokenizedDocument(textData);

Visualize the documents using a word cloud.

figure wordcloud(documents);

Figure contains an object of type wordcloud.

filename = "sonnetsPreprocessed.txt"; str = extractFileText(filename); textData = split(str,newline); documents = tokenizedDocument(textData);

Create a bag-of-words model using bagOfWords.

bag = bagOfWords(documents)

bag = bagOfWords with properties:

      Counts: [154×3092 double]
  Vocabulary: ["fairest"    "creatures"    "desire"    "increase"    "thereby"    "beautys"    "rose"    "might"    "never"    "die"    "riper"    "time"    "decease"    "tender"    "heir"    "bear"    "memory"    "thou"    "contracted"    …    ]
    NumWords: 3092
NumDocuments: 154

Visualize the bag-of-words model using a word cloud.

Figure contains an object of type wordcloud.

Load the example data sonnetsTable. The table tbl contains a list of words in the variable Word, and the corresponding frequency counts in the variable Count.

load sonnetsTable head(tbl)

   Word        Count
___________    _____

{'''tis'  }      1  
{''Amen'' }      1  
{''Fair'  }      2  
{''Gainst'}      1  
{''Since' }      1  
{''This'  }      2  
{''Thou'  }      1  
{''Thus'  }      1

Plot the table data using wordcloud. Specify the words and corresponding word sizes to be the Word and Count variables respectively.

figure wordcloud(tbl,'Word','Count'); title("Sonnets Word Cloud")

Figure contains an object of type wordcloud. The chart of type wordcloud has title Sonnets Word Cloud.

To reproduce the results in this example, set rng to 'default'.

filename = "sonnetsPreprocessed.txt"; str = extractFileText(filename); textData = split(str,newline); documents = tokenizedDocument(textData);

Create a bag-of-words model using bagOfWords.

bag = bagOfWords(documents)

bag = bagOfWords with properties:

      Counts: [154×3092 double]
  Vocabulary: ["fairest"    "creatures"    "desire"    "increase"    "thereby"    "beautys"    "rose"    "might"    "never"    "die"    "riper"    "time"    "decease"    "tender"    "heir"    "bear"    "memory"    "thou"    "contracted"    …    ]
    NumWords: 3092
NumDocuments: 154

Fit an LDA model with 20 topics. To suppress verbose output, set 'Verbose' to 0.

mdl = fitlda(bag,20,'Verbose',0)

mdl = ldaModel with properties:

                 NumTopics: 20
         WordConcentration: 1
        TopicConcentration: 5
  CorpusTopicProbabilities: [0.0500 0.0500 0.0500 0.0500 0.0500 0.0500 0.0500 0.0500 0.0500 0.0500 0.0500 0.0500 0.0500 0.0500 0.0500 0.0500 0.0500 0.0500 0.0500 0.0500]
DocumentTopicProbabilities: [154×20 double]
    TopicWordProbabilities: [3092×20 double]
                Vocabulary: ["fairest"    "creatures"    "desire"    "increase"    "thereby"    "beautys"    "rose"    "might"    "never"    "die"    "riper"    "time"    "decease"    "tender"    "heir"    "bear"    "memory"    "thou"    …    ]
                TopicOrder: 'initial-fit-probability'
                   FitInfo: [1×1 struct]

Visualize the first four topics using word clouds.

figure for topicIdx = 1:4 subplot(2,2,topicIdx) wordcloud(mdl,topicIdx); title("Topic: " + topicIdx) end

Figure contains objects of type wordcloud. The chart of type wordcloud has title Topic: 1. The chart of type wordcloud has title Topic: 2. The chart of type wordcloud has title Topic: 3. The chart of type wordcloud has title Topic: 4.

Input Arguments

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Input text, specified as a string array, character vector, or cell array of character vectors.

For string input, the wordcloud and wordCloudCounts functions use English, Japanese, German, and Korean tokenization, stop word removal, and word normalization.

Example: ["an example of a short document";"a second short document"]

Data Types: string | char | cell

Input table, with columns specifying the words and word sizes. Specify the words and the corresponding word sizes in the variables given by wordVar andsizeVar input arguments respectively.

Data Types: table

Table variable for word data, specified as a string scalar, character vector, numeric index, or a logical vector.

Table variable for size data, specified as a string scalar, character vector, numeric index, or a logical vector.

Input words, specified as a string vector or cell array of character vectors.

Data Types: string | cell

Word size data, specified as a numeric vector.

Input categorical data, specified as a categorical array. The function plots each unique element of C with size corresponding to histcounts(C).

Data Types: categorical

Input bag-of-words or bag-of-n-grams model, specified as a bagOfWords object or a bagOfNgrams object. If bag is abagOfNgrams object, then the function treats each n-gram as a single word.

Input LDA model, specified as an ldaModel object.

Index of LDA topic, specified as a nonnegative integer.

Parent, specified as a figure, panel, or tab.

Name-Value Arguments

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Specify optional pairs of arguments asName1=Value1,...,NameN=ValueN, where Name is the argument name and Value is the corresponding value. Name-value arguments must appear after other arguments, but the order of the pairs does not matter.

Before R2021a, use commas to separate each name and value, and enclose Name in quotes.

Example: 'HighlightColor','blue' specifies the highlight color to be blue.

The WordCloudChart properties listed here are only a subset. For a complete list, see WordCloudChart Properties.

Maximum number of words to display, specified as a non-negative integer. The software displays the MaxDisplayWords largest words.

Word color, specified as an RGB triplet, a character vector containing a color name, or an N-by-3 matrix where N is the length ofWordData. If Color is a matrix, then each row corresponds to an RGB triplet for the corresponding word inWordData.

RGB triplets and hexadecimal color codes are useful for specifying custom colors.

An RGB triplet is a three-element row vector whose elements specify the intensities of the red, green, and blue components of the color. The intensities must be in the range [0,1]; for example, [0.4 0.6 0.7].
A hexadecimal color code is a character vector or a string scalar that starts with a hash symbol (#) followed by three or six hexadecimal digits, which can range from 0 to F. The values are not case sensitive. Thus, the color codes"#FF8800", "#ff8800","#F80", and "#f80" are equivalent.

Alternatively, you can specify some common colors by name. This table lists the named color options, the equivalent RGB triplets, and hexadecimal color codes.

Color Name	Short Name	RGB Triplet	Hexadecimal Color Code
"red"	"r"	[1 0 0]	"#FF0000"
"green"	"g"	[0 1 0]	"#00FF00"
"blue"	"b"	[0 0 1]	"#0000FF"
"cyan"	"c"	[0 1 1]	"#00FFFF"
"magenta"	"m"	[1 0 1]	"#FF00FF"
"yellow"	"y"	[1 1 0]	"#FFFF00"
"black"	"k"	[0 0 0]	"#000000"
"white"	"w"	[1 1 1]	"#FFFFFF"

This table lists the default color palettes for plots in the light and dark themes.

Palette	Palette Colors
"gem" — Light theme default_Before R2025a: Most plots use these colors by default._
"glow" — Dark theme default

You can get the RGB triplets and hexadecimal color codes for these palettes using the orderedcolors and rgb2hex functions. For example, get the RGB triplets for the "gem" palette and convert them to hexadecimal color codes.

RGB = orderedcolors("gem"); H = rgb2hex(RGB);

Before R2023b: Get the RGB triplets using RGB = get(groot,"FactoryAxesColorOrder").

Before R2024a: Get the hexadecimal color codes using H = compose("#%02X%02X%02X",round(RGB*255)).

Example: 'blue'

Example: [0 0 1]

Word highlight color, specified as an RGB triplet, or a character vector containing a color name. The software highlights the largest words with this color.

RGB triplets and hexadecimal color codes are useful for specifying custom colors.

An RGB triplet is a three-element row vector whose elements specify the intensities of the red, green, and blue components of the color. The intensities must be in the range [0,1]; for example, [0.4 0.6 0.7].
A hexadecimal color code is a character vector or a string scalar that starts with a hash symbol (#) followed by three or six hexadecimal digits, which can range from 0 to F. The values are not case sensitive. Thus, the color codes"#FF8800", "#ff8800","#F80", and "#f80" are equivalent.

Alternatively, you can specify some common colors by name. This table lists the named color options, the equivalent RGB triplets, and hexadecimal color codes.

Color Name	Short Name	RGB Triplet	Hexadecimal Color Code
"red"	"r"	[1 0 0]	"#FF0000"
"green"	"g"	[0 1 0]	"#00FF00"
"blue"	"b"	[0 0 1]	"#0000FF"
"cyan"	"c"	[0 1 1]	"#00FFFF"
"magenta"	"m"	[1 0 1]	"#FF00FF"
"yellow"	"y"	[1 1 0]	"#FFFF00"
"black"	"k"	[0 0 0]	"#000000"
"white"	"w"	[1 1 1]	"#FFFFFF"

This table lists the default color palettes for plots in the light and dark themes.

Palette	Palette Colors
"gem" — Light theme default_Before R2025a: Most plots use these colors by default._
"glow" — Dark theme default

RGB = orderedcolors("gem"); H = rgb2hex(RGB);

Before R2023b: Get the RGB triplets using RGB = get(groot,"FactoryAxesColorOrder").

Before R2024a: Get the hexadecimal color codes using H = compose("#%02X%02X%02X",round(RGB*255)).

Example: 'blue'

Example: [0 0 1]

Shape of word cloud chart, specified as 'oval' or 'rectangle'.

Example: 'rectangle'

More About

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For string input, the wordcloud and wordCloudCounts functions use English, Japanese, German, and Korean tokenization, stop word removal, and word normalization.

For other languages, you might need to manually preprocess your text data and specify unique words and corresponding sizes in wordcloud.

To specify word sizes in wordcloud, input your data as a table or arrays containing the unique words and corresponding sizes.

Version History

Introduced in R2017b