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Text analysis api

Project Description

This is a Python client for the Bookworm natural language API, made available by Crawlica.

You can register to receive an API key at

Example usage

import tabulate
from bookworm import Bookworm

API_KEY = '...'

documents = ['De två största städerna i Sverige är Stockholm och Göteborg',
             'Donald Trump blir Barack Obamas efterträdare']

b = Bookworm(API_KEY, 'sv')
res = b.entities(documents)

for ents in res:
    data = [[ent['entity'], ent['type'], ', '.join(ent['variants'])] for ent in ents]
    print(tabulate.tabulate(data, headers=['Entity', 'Type', 'Variants']))
Entity     Type    Variants
---------  ------  ----------
Sverige    geo     Sverige
Stockholm  geo     Stockholm
Göteborg   geo     Göteborg

Entity        Type    Variants
------------  ------  -------------
Donald Trump  person  Donald Trump
Barack Obama  person  Barack Obamas


$ pip install bookworm

Functionality currently available

  • autotag - Automatically tag documents with the best matching tags from our large database of tags.
  • wordsmash - Compare document sets to get the essence of what makes a subset special. Get the defining words and phrases.
  • entities - Extract named entities (people, organisations, and places) from each document
  • sentiment - Get a numerical representation of how positive or negative each document is
  • wordcount - Get a count of the distinct words in the documents. Declinations are grouped into the base form and stop words can be removed.
  • cluster - Automatically collect documents into groups, based on their topics, and describe those groups.
  • categorize - Automatically put documents in a set of categories that you choose. The categories are defined by topic words, but the documents need not contain any of those words, just “close” words.
Release History

Release History

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Download Files

Download Files

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File Name & Checksum SHA256 Checksum Help Version File Type Upload Date
bookworm-0.2.1.tar.gz (3.4 kB) Copy SHA256 Checksum SHA256 Source Mar 23, 2017

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