Word Count

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In order to count the words in your document you can use the TextChunk class Word count tool. Lettria’s word count tool enables you to quickly and easily extract the word, part of speech and occurrences within your document. You can also filter out those words by POS tags to enable further analysis of the words within your document.

After you've installed the Lettria package on Python you'll need to import the library.

import lettria

Next you are going to need to include your personal API key which can be found

via the Lettria platform in the dashboard.

api_key = 'your personal API key'
nlp = lettria.NLP(api_key)

Now you will need to open your saved document. Be sure to add the name of

‘your file’ since it may differ from the name of my example file.

with open("example.txt", "r") as f:
example_data = f.readlines()

Next, add the document to the NLP.

nlp.add_document(example_data)

In order to extract the words of the document use the following command:

word_count = nlp.word_count
word_count(filter_pos = None, lemma=False)

In the results you will have a list of words, part of speech and number of occurrences.

If you would like to filter out parts of speech such as ‘noun’ you can change the filter criteria.

word_count = nlp.word_count
word_count(filter_pos = ’N’, lemma=False)

Now you can see within the results all the words and number of occurrences falling under ‘noun’ has been filtered out.

In order to save your results you can use the following command.

nlp.save_results(‘example_results')

And a json file with your results that can be used for further analysis will be saved.

import lettria

api_key = 'your personal API key'
nlp = lettria.NLP(api_key)

with open("example.txt", "r") as f:
example_data = f.readlines()

nlp.add_document(example_data)

word_count = nlp.word_count
word_count(filter_pos = None, lemma=False)

word_count = nlp.word_count
word_count(filter_pos = ’N’, lemma=False)

nlp.save_results(‘example_results')