def azureTone()

in src/plugins/utils/tone.py [0:0]

• 46 lines of code
• 13 McCabe index (conditional complexity)

def azureTone(KibbleBit, bodies):
    """ Sentiment analysis using Azure Text Analysis API """
    if 'azure' in KibbleBit.config:
        headers = {
            'Content-Type': 'application/json',
            'Ocp-Apim-Subscription-Key': KibbleBit.config['azure']['apikey']
        }
        
        
        js = {
            "documents": []
          }
        
        # For each body...
        a = 0
        moods = []
        for body in bodies:
            # Crop out quotes
            lines = body.split("\n")
            body = "\n".join([x for x in lines if not x.startswith(">")])
            doc = {
                "language": "en",
                "id": str(a),
                "text": body
              }
            js['documents'].append(doc)
            moods.append({}) # placeholder for each doc, to be replaced
            a += 1
        try:
            rv = requests.post(
                "https://%s.api.cognitive.microsoft.com/text/analytics/v2.0/sentiment" % KibbleBit.config['azure']['location'],
                headers = headers,
                data = json.dumps(js)
            )
            jsout = rv.json()
        except:
            jsout = {} # borked sentiment analysis?
        
        if 'documents' in jsout and len(jsout['documents']) > 0:
            for doc in jsout['documents']:
                mood = {}
                # This is more parred than Watson, so we'll split it into three groups: positive, neutral and negative.
                # Divide into four segments, 0->40%, 25->75% and 60->100%.
                # 0-40 promotes negative, 60-100 promotes positive, and 25-75% promotes neutral.
                # As we don't want to over-represent negative/positive where the results are
                # muddy, the neutral zone is larger than the positive/negative zones by 10%.
                val = doc['score']
                mood['negative'] = max(0, ((0.4 - val) * 2.5)) # For 40% and below, use 2½ distance
                mood['positive'] = max(0, ((val-0.6) * 2.5)) # For 60% and above, use 2½ distance
                mood['neutral'] = max(0, 1 - (abs(val - 0.5) * 2)) # Between 25% and 75% use double the distance to middle.
                moods[int(doc['id'])] = mood # Replace moods[X] with the actual mood
                
        else:
            KibbleBit.pprint("Failed to analyze email body.")
            print(jsout)
            # Depending on price tier, Azure will return a 429 if you go too fast.
            # If we see a statusCode return, let's just stop for now.
            # Later scans can pick up the slack.
            if 'statusCode' in jsout:
                KibbleBit.pprint("Possible rate limiting in place, stopping for now.")
                return False
        return moods