Create an array of two bags-of-words models from tokenized documents.

str = [ ...
    "an example of a short sentence"
    "a second short sentence"];
documents = tokenizedDocument(str);
bag(1) = bagOfWords(documents(1));
bag(2) = bagOfWords(documents(2))

bag=1×2 object
  1x2 bagOfWords array with properties:

    Counts
    Vocabulary
    NumWords
    NumDocuments

Combine the bag-of-words models using join.

bag = join(bag)

bag = 
  bagOfWords with properties:

          Counts: [2x7 double]
      Vocabulary: [1x7 string]
        NumWords: 7
    NumDocuments: 2

If your text data is contained in multiple files in a folder, then you can import the text data and create a bag-of-words model in parallel using parfor. If you have Parallel Computing Toolbox™ installed, then the parfor loop runs in parallel, otherwise, it runs in serial. Use join to combine an array of bag-of-words models into one model.

Create a bag-of-words model from a collection of files. The examples sonnets have file names "exampleSonnetN.txt", where N is the number of the sonnet. Get a list of the files and their locations using dir.

fileLocation = fullfile(matlabroot,'examples','textanalytics','exampleSonnet*.txt');
fileInfo = dir(fileLocation)

fileInfo = 

  0x1 empty struct array with fields:

    name
    folder
    date
    bytes
    isdir
    datenum

Initialize an empty bag-of-words model and then loop over the files and create an array of bag-of-words models.

bag = bagOfWords;

numFiles = numel(fileInfo);
parfor i = 1:numFiles
    f = fileInfo(i);
    filename = fullfile(f.folder,f.name);
    
    textData = extractFileText(filename);
    document = tokenizedDocument(textData);
    bag(i) = bagOfWords(document);
end

Combine the bag-of-words models using join.

bag = join(bag)

bag = 
  bagOfWords with properties:

          Counts: []
      Vocabulary: [1x0 string]
        NumWords: 0
    NumDocuments: 0