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A text metrics vector created with Turbo360
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This project was built with Turbo 360. To learn more, click here:


After cloning into repo, cd to project root directory and run npm install:

$ npm install

To run dev server, install Turbo CLI globally:

$ sudo npm install turbo-cli -g

Then run devserver from project root directory:

$ turbo devserver

To build for production, run build:

$ npm run build


Text Metrics takes a block of text and returns various information about it including word frequencies, sentiment, and processed versions of the text. The base endpoint is:
Query Required Options
text yes any string
lang no More information below

Example 1 - GET Request

You should access this vector with a GET request if you only have a small amount of text to process (< 2000 characters), and you want to use your browser to see the results.

Step 1 (Optional)

Enter the language into the following endpoint as the 'lang' query parameter:, how are you doing today?

The language parameter is used for proper removal of stopwords. If this parameter is omitted, it will default to English. The available languages are:

lang Language
ar Modern Standard Arabic
bn Bengali
br Brazilian Portuguese
da Danish
de German
en English
es Spanish
fa Farsi
fr French
hi Hindi
it Italian
ja Japanese
nl Dutch
no Norwegian
pl Polish
pt Portuguese
ru Russian
sv Swedish
zh Chinese Simplified

Step 2

Find text that you would like to be processed, or use a simple example such as "Hello, how are you doing today?"

Enter the text into the following endpoint as the 'text' query parameter:, how are you doing today?

JSON Payload

The JSON payload will contain the following information:

numWords The total number of words in the original text
numLetters The total number of letter in the original text
averageWordLength The average length of all words in the original text
numUniqueWords The number of unique words in the original text
wordOccurences A dictionary of all words in the text (except stopwords) and each word's frequency
topTenWords A dictionary of the top 10 most frequent words in the text and each word's frequency
score The AFINN-based sentiment score for the given text
comparative The AFINN-based comparative for the given text
positive A list of the positive words in the text
negative A list of the negative words in the text
textOriginal The original text supplied as a query parameter
textSimplified The text after converting to lowercase, removing all non-alphanumeric characters, and converting all white spaces to simple spaces
textSansStopwords The text after being simplified and having all stopwords removed
all All the words in the original text in array form with repetitions
unique All unique words in the original text in array form
uniqueSansStopwords All unique words in the original text without stopwords in array form

Recommended Use

Use this vector to automatically process and analyze comments, reviews, blog posts, etc. as they come in on your website.

You can’t perform that action at this time.