mirror of
https://github.com/wassname/IndicoIo-python.git
synced 2026-06-29 16:30:39 +08:00
Madison May
69 lines
2.4 KiB
Python
69 lines
2.4 KiB
Python
import requests
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import json
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from indicoio import JSON_HEADERS
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from indicoio.utils import normalize
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def political(api_root, text):
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"""
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Given input text, returns a probability distribution over the political alignment of the speaker.
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Example usage:
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.. code-block:: python
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>>> from indicoio import political
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>>> import numpy as np
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>>> text = 'Wish we had more bike lanes. \
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Hopefully, driverless cars will chance economics from ownership to fee for service.'
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>>> affiliation = political(text)
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>>> affiliation
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{u'Libertarian': 0.4923755446986322, u'Green': 0.2974443102818122,
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u'Liberal': 0.13730032938784784, u'Conservative': 0.07287981563170784}
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>>> least_like = affiliation.keys()[np.argmin(affiliation.values())]
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>>> most_like = affiliation.keys()[np.argmax(affiliation.values())]
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>>> 'This text is most like %s and least like %s'%(most_like,least_like)
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u'This text is most like Libertarian and least like Conservative'
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:param text: The text to be analyzed.
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:type text: str or unicode
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:rtype: Dictionary of party probability pairs
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"""
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data_dict = json.dumps({'text': text})
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response = requests.post(api_root + "political", data=data_dict, headers=JSON_HEADERS)
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response_dict = response.json()
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if len(response_dict) < 2:
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raise ValueError(response_dict.values()[0])
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else:
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return response_dict
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def posneg(api_root, text):
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"""
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Given input text, returns a scalar estimate of the sentiment of that text.
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Values are roughly in the range 0 to 1 with 0.5 indicating neutral sentiment.
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For reference, 0 suggests very negative sentiment and 1 suggests very positive sentiment.
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Example usage:
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.. code-block:: python
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>>> from indicoio import sentiment
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>>> text = 'Thanks everyone for the birthday wishes!! It was a crazy few days ><'
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>>> sentiment = sentiment(text)
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>>> sentiment
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0.6946439339979863
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:param text: The text to be analyzed.
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:type text: str or unicode
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:rtype: Float
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"""
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data_dict = json.dumps({'text': text})
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response = requests.post(api_root + "sentiment", data=data_dict, headers=JSON_HEADERS)
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response_dict = response.json()
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if 'Sentiment' not in response_dict:
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raise ValueError(response_dict.values()[0])
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else:
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return response_dict['Sentiment']
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