wordcloud

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

Syntax

wordcloud(str)

wordcloud(documents)

wordcloud(bag)

wordcloud(tbl,wordVar,sizeVar)

wordcloud(words,sizeData)

wordcloud(C)

wordcloud(ldaMdl,topicIdx)

wordcloud(___,Name,Value)

wordcloud(parent,___)

wc = wordcloud(___)

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) 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.

wordcloud(documents) creates a word cloud chart from the words appearing in documents.

wordcloud(bag) creates a word cloud chart from the bag-of-words or bag-of-n-grams model bag.

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

wordcloud(words,sizeData) creates a word cloud chart from elements of words with word sizes specified by sizeData.

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

wordcloud(ldaMdl,topicIdx) creates a word cloud chart from the topic with index topicIdx of the LDA model ldaMdl.

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

wordcloud(parent,___) creates the word cloud in the figure, panel, or tab specified by parent.

wc = 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

Create Word Cloud from Text Data

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
wordcloud(str);

Create Word Cloud from Tokenized Documents

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);

Create Word Cloud from Bag-of-Words Model

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);

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
wordcloud(bag);

Create Word Cloud from Table

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")

Create Word Cloud from LDA Topic

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

rng('default')

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);

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

Input Arguments

str — Input text

string array | character vector | cell array of character vectors

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

documents — Input documents

tokenizedDocument array

Input documents, specified as a tokenizedDocument array.

tbl — Input table

table

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

Data Types: table

wordVar — Table variable for word data

string scalar | character vector | numeric index | logical vector

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

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64 | logical | char | string

sizeVar — Table variable for size data

string scalar | character vector | numeric index | logical vector

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

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64 | logical | char | string

words — Input words

string vector | cell array of character vectors

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

Data Types: string | cell

sizeData — Word size data

numeric vector

Word size data, specified as a numeric vector.

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

C — Input categorical data

categorical array

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

bag — Input model

bagOfWords object | bagOfNgrams object

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

ldaMdl — Input LDA model

ldaModel object

Input LDA model, specified as an ldaModel object.

topicIdx — Index of LDA topic

nonnegative integer

Index of LDA topic, specified as a nonnegative integer.

parent — Parent

figure | panel | tab

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

Name-Value Arguments

Specify optional pairs of arguments as Name1=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.

MaxDisplayWords — Maximum number of words to display

100 (default) | nonnegative integer

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

Color — Word color

[0.3804 0.3804 0.3804] (default) | RGB triplet | character vector containing a color name | matrix

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 of WordData. If Color is a matrix, then each row corresponds to an RGB triplet for the corresponding word in WordData.

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	Appearance
"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"

Here are the RGB triplets and hexadecimal color codes for the default colors MATLAB uses in many types of plots.

RGB Triplet	Hexadecimal Color Code	Appearance
[0 0.4470 0.7410]	"#0072BD"
[0.8500 0.3250 0.0980]	"#D95319"
[0.9290 0.6940 0.1250]	"#EDB120"
[0.4940 0.1840 0.5560]	"#7E2F8E"
[0.4660 0.6740 0.1880]	"#77AC30"
[0.3010 0.7450 0.9330]	"#4DBEEE"
[0.6350 0.0780 0.1840]	"#A2142F"

Example: 'blue'

Example: [0 0 1]

HighlightColor — Word highlight color

[0.7529 0.2980 0.0431] (default) | RGB triplet | character vector containing a color name

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	Appearance
"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"

Here are the RGB triplets and hexadecimal color codes for the default colors MATLAB uses in many types of plots.

RGB Triplet	Hexadecimal Color Code	Appearance
[0 0.4470 0.7410]	"#0072BD"
[0.8500 0.3250 0.0980]	"#D95319"
[0.9290 0.6940 0.1250]	"#EDB120"
[0.4940 0.1840 0.5560]	"#7E2F8E"
[0.4660 0.6740 0.1880]	"#77AC30"
[0.3010 0.7450 0.9330]	"#4DBEEE"
[0.6350 0.0780 0.1840]	"#A2142F"

Example: 'blue'

Example: [0 0 1]

Shape — Shape of word cloud

'oval' (default) | 'rectangle'

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

Example: 'rectangle'

Output Arguments

wc — WordCloudChart object

WordCloudChart object

WordCloudChart object. You can modify the properties of a WordCloudChart after it is created. For more information, see WordCloudChart Properties.

More About

Language Considerations

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