[kibble-scanners] 01/02: add support for picoAPI sentiment analysis

[hu...@apache.org]

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humbedooh pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/kibble-scanners.git

commit 0d3300b0eeba4e89a001d6aa01b1ed73e409c02a
Author: Daniel Gruno <hu...@apache.org>
AuthorDate: Fri Dec 8 13:11:05 2017 +0100

    add support for picoAPI sentiment analysis
    
    The more the merrier
---
 src/plugins/utils/tone.py | 63 +++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 63 insertions(+)

diff --git a/src/plugins/utils/tone.py b/src/plugins/utils/tone.py
index 9c26809..631bbb9 100644
--- a/src/plugins/utils/tone.py
+++ b/src/plugins/utils/tone.py
@@ -133,4 +133,67 @@ def azureTone(KibbleBit, bodies):
                 KibbleBit.pprint("Possible rate limiting in place, stopping for now.")
                 return False
         return moods
+    
+def picoTone(KibbleBit, bodies):
+    """ Sentiment analysis using picoAPI Text Analysis """
+    if 'picoapi' in KibbleBit.config:
+        headers = {
+            'Content-Type': 'application/json',
+            'PicoAPI-Key': KibbleBit.config['picoapi']['key']
+        }
+        
+        
+        js = {
+            "texts": []
+          }
+        
+        # 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 = {
+                "id": str(a),
+                "body": body
+              }
+            js['texts'].append(doc)
+            moods.append({}) # placeholder for each doc, to be replaced
+            a += 1
+        try:
+            rv = requests.post(
+                "https://v1.picoapi.com/api/text/sentiment",
+                headers = headers,
+                data = json.dumps(js)
+            )
+            jsout = rv.json()
+        except:
+            jsout = {} # borked sentiment analysis?
+        
+        if 'results' in jsout and len(jsout['results']) > 0:
+            for doc in jsout['results']:
+                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 = (1 + doc['sentiment']) / 2 # This ranges from -1 to +1, so we'll just harmonize that to 0->1 and use the azure calcs
+                
+                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)
+            # 403 returned on invalid key, 429 on rate exceeded.
+            # If we see a code return, let's just stop for now.
+            # Later scans can pick up the slack.
+            if 'code' in jsout:
+                KibbleBit.pprint("Possible rate limiting in place, stopping for now.")
+                return False
+        return moods
     
\ No newline at end of file

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