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Version: 2.0

Subsentence Class

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What is the subsentence class?

Subsentence stores data relative to a part of a sentence. For longer and more complicated sentences it can be advantageous to cut it in multiple pieces to have a more detailed analysis.

If you are looking for more about subsentence class check out our documentation 👨🏻‍💻

Importing the library & your personal API key

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

Adding data

Next add data to be analyzed. You can also upload a saved document using the ‘with open’ command.

data = ‘example text’

Next add the data to the NLP.

nlp.add_document(data)

Extracting subsentences

In order to extract the subsentences in your document use the following command:

subsentences = nlp.subsentences
print(subsentences)

Extracting properties

The subsentence class also contains the token property. In order to extract tokens use the following command:

tokens = nlp.tokens
print(tokens)

Lastly you can extract common properties with the Subsentence class.

Some popular common properties include; lemma, pos, language etc. To have a full list of common properties check out our documentation.

A useful common property that could come in handy while extracting and analyzing elements is the sentiment property.

sentiment = nlp.sentiment
print(sentiment)

For my sentence I have the values for positive, negative and total. Values above 0 are positive and values below 0 are negative.

Saving your results

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.

Code set

import lettria

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

data = ‘example text’

nlp.add_document(data)

subsentences = nlp.subsentences
print(subsentences)

tokens = nlp.tokens
print(tokens)

sentiment = nlp.sentiment
print(sentiment)

nlp.save_results(‘example_results')