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A Gavagai API helper library.

Project description

A Gavagai API helper library.

https://travis-ci.org/dewe/gavagai-python.svg?branch=master

Install

$ pip install gavagai

Api key

Get your own api key for free at Gavagai Developer Portal.

The api key can be specified when instantiating the client, see examples below. Alternatively, you can set the GAVAGAI_APIKEY environment variable, and just call GavagaiClient().

API methods

Gavagai Rest API methods supported by this version:

  • /keywords - Extract salient concepts from a collection of texts.
  • /lexicon - look up a word in Gavagai Living Lexicon.
  • /tonality - Multidimensional sentiment analysis.
  • /topics & /stories - Multi-text summarization: get the gist of your text collection without having to read through every single sentence.

Use

See Gavagai API documentation for details about available API resources.

Example: API call on a set of texts

The /keywords resource extracts salient concepts from a collection of texts. Order by number of occurrences.

from gavagai.client import GavagaiClient
from pprint import pprint

texts = [
    'Stayed here for 3 nights at the beginning of a trip of California. Could not say enough good things about the hotel Monaco. Amazing staff, amazing rooms and the location is brilliant! First stay at a Kimpton hotel, but definitely not the last!!!',
    'I did a lot of research looking for a hotel suite for our family vacation in San Francisco. The Hotel Monaco was a perfect choice. What friendly and delightful staff. I will miss the Grand Cafe, but I will make sure to come back to see their new offerings.',
    'My partner and I spent four nights here over New Years and loved it. Super staff; lovely, quiet room; excellent location within easy walking to much of Downtown and an overall experience that was perfect.'
]

client = GavagaiClient('use_your_own_api_key')
result = client.keywords(texts)
keywords = result.json()

pprint(keywords)

Example: API call with language specified

The /tonality resource measures multi-dimensional sentiment, based on lexical analysis. Default language is English, but for texts in other languages, the language option should be specified.

rom gavagai.client import GavagaiClient
from pprint import pprint

texts = [u'Din idiot!', u'Jag älskar dig.', u'Hen hatar det.']

client = GavagaiClient('use_your_own_api_key')
result = client.tonality(texts, language='sv') # swedish language option
keywords = result.json()

pprint(keywords)

Set up for development

From root of this repository:

$ pip install -r requirements.txt

Run tests

$ py.test

Releasing

I use zest.releaser for releasing this package.

Project details


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