implement API configuration and and update ontology scripts

NLTK_model/API.py

+import numpy
+from flask import Flask
+from flask import request
+from flask_cors import CORS, cross_origin
+
+import Ontology_scripts
+
+app = Flask(__name__)
+cors = CORS(app)
+
+@app.route("/predict", methods =['POST'])
+@cross_origin()
+def predict():
+    data = request.get_json()
+    print(data['title'])
+    print(data['content'])
+    prediction = Ontology_scripts.analyzePost(data['title'], data['content'], data['reviews'])
+    return {"pred": str(prediction)}
+
+if __name__ == '__main__':
+    app.run(debug=True, port=5002, host="0.0.0.0")

NLTK_model/Most Informative Features.txt

+		      :( = True           Negati : Positi =   2647.8 : 1.0
+                      :) = True           Positi : Negati =   1272.4 : 1.0
+                follower = True           Positi : Negati =     26.6 : 1.0
+                     bam = True           Positi : Negati =     26.3 : 1.0
+                     sad = True           Negati : Positi =     25.0 : 1.0
+                     x15 = True           Negati : Positi =     23.0 : 1.0
+                followed = True           Negati : Positi =     19.8 : 1.0
+              appreciate = True           Positi : Negati =     19.0 : 1.0
+               community = True           Positi : Negati =     18.3 : 1.0
+                     ugh = True           Negati : Positi =     16.3 : 1.0
+                    glad = True           Positi : Negati =     15.8 : 1.0
+               goodnight = True           Positi : Negati =     15.0 : 1.0
+                 welcome = True           Positi : Negati =     14.6 : 1.0
+                 perfect = True           Positi : Negati =     13.0 : 1.0
+                    kill = True           Negati : Positi =     13.0 : 1.0
+
+
+What this tells you is the ratio of occurences in negative to positive, or visa versa, for every word. So here, we can see that the term ":(" appears 2647.8 more times as often in negative reviews as it does in positive reviews.
\ No newline at end of file

NLTK_model/Ontology_scripts.py

@@ -97,13 +97,13 @@ def analyzePost(title, content, reviews):
     if titleSentiment == "Positive":
         points += 2
     else:
-        points -= 2
+        points -= 0
     content_tokens = remove_noise(word_tokenize(content))
     contentSentiment = classifier.classify(dict([token, True] for token in content_tokens))
     if contentSentiment == "Positive":
         points += 5
     else:
-        points -= 5
+        points -= 0
     points += len(reviews)
     for review in reviews:
         review_tokens = remove_noise(word_tokenize(review))
@@ -111,7 +111,7 @@ def analyzePost(title, content, reviews):
         if reviewSentiment == "Positive":
             points += 1
         else:
-            points -= 1
+            points -= 0
     return points
 
-print(analyzePost("watch your dog's health", "bvjkdn dbfjksdn", ["gdfsdff"]))
+# print(analyzePost("watch your dog's health", "bvjkdn dbfjksdn", ["gdfsdff"]))