Sentiment Analysis

#This imports some required packages.
#import utils #Often I'll develop functions in a notebook then move to utils. 
import pandas as pd
import urllib.request
import json
import pprint

#I typically store my config values. 
azure_text_endpoint= 'https://eastus.api.cognitive.microsoft.com/text/analytics/v2.0'
azure_text_key1= 'key posted on slack'

def azure_request(command, endpoint, key, postdata):
    #Set URI
    uri=endpoint+"/"+command
    #Set header
    headers = {}
    headers['Ocp-Apim-Subscription-Key'] = key
    headers['Content-Type'] = 'application/json'
    headers['Accept'] = 'application/json'
    #Make request
    request = urllib.request.Request(uri, postdata, headers)
    response = urllib.request.urlopen(request)
    return json.loads(response.read().decode('utf-8'))

def df_to_postdata(df):
    return json.dumps({'documents': json.loads(df.to_json(orient='records')) }).encode('utf-8')

#First lets test with sample data from the examples. 
#https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/quickstarts/python
test_data = json.dumps({ 'documents': [
    { 'id': '1', 'language': 'en', 'text': 'I really enjoy the new XBox One S. It has a clean look, it has 4K/HDR resolution and it is affordable.' },
    { 'id': '2', 'language': 'es', 'text': 'Este ha sido un dia terrible, llegué tarde al trabajo debido a un accidente automobilistico.' }
]}).encode('utf-8')
pprint.pprint(test_data)

test_result=azure_request('sentiment', azure_text_endpoint, azure_text_key1, test_data)
test_result