commiting sentiment analysis using IBM waston NLU

Author: hrzhang001

sentiment_analysis.py

@@ -0,0 +1,89 @@
+#By Haoran Zhang
+from watson_developer_cloud import NaturalLanguageUnderstandingV1
+from watson_developer_cloud.natural_language_understanding_v1 \
+  import Features, EntitiesOptions, KeywordsOptions, EmotionOptions, SentimentOptions
+import pandas as pd
+
+natural_language_understanding = NaturalLanguageUnderstandingV1(
+  username='{username}',
+  password='{password}',
+  version='2018-03-16')
+
+data = pd.read_csv("all_result.csv")
+sadness=[]
+joy=[]
+fear=[]
+disgust=[]
+anger=[]
+sentiment_score=[]
+p_sadness=[]
+p_joy=[]
+p_fear=[]
+p_disgust=[]
+p_anger=[]
+p_sentiment_score=[]
+j = 0
+for i in data['body']:
+  print("Analyzing original post:",j)
+  j=j + 1
+  try:
+    response = natural_language_understanding.analyze(
+        text=i,
+        features=Features(
+          emotion=EmotionOptions(
+            document=True),
+        sentiment=SentimentOptions(
+            document=True)))
+    sadness.append(response['emotion']['document']['emotion']['sadness'])
+    joy.append(response['emotion']['document']['emotion']['joy'])
+    fear.append(response['emotion']['document']['emotion']['fear'])
+    disgust.append(response['emotion']['document']['emotion']['disgust'])
+    anger.append(response['emotion']['document']['emotion']['anger'])
+    sentiment_score.append(response['sentiment']['document']['score'])
+  except:
+    sadness.append('NA')
+    joy.append('NA')
+    fear.append('NA')
+    disgust.append('NA')
+    anger.append('NA')
+    sentiment_score.append('NA')
+data['sadness']=sadness
+data['joy']=joy
+data['fear']=fear
+data['disgust']=disgust
+data['anger']=anger
+data['sentiment_score']=sentiment_score
+
+j = 0
+for i in data['p_body']:
+  print("Analyzing parent posts:",j)
+  j=j + 1
+  try:
+    response = natural_language_understanding.analyze(
+        text=i,
+        features=Features(
+          emotion=EmotionOptions(
+            document=True),
+        sentiment=SentimentOptions(
+            document=True)))
+    p_sadness.append(response['emotion']['document']['emotion']['sadness'])
+    p_joy.append(response['emotion']['document']['emotion']['joy'])
+    p_fear.append(response['emotion']['document']['emotion']['fear'])
+    p_disgust.append(response['emotion']['document']['emotion']['disgust'])
+    p_anger.append(response['emotion']['document']['emotion']['anger'])
+    p_sentiment_score.append(response['sentiment']['document']['score'])
+  except:
+    p_sadness.append('NA')
+    p_joy.append('NA')
+    p_fear.append('NA')
+    p_disgust.append('NA')
+    p_anger.append('NA')
+    p_sentiment_score.append('NA')
+data['p_sadness']=p_sadness
+data['p_joy']=p_joy
+data['p_fear']=p_fear
+data['p_disgust']=p_disgust
+data['p_anger']=p_anger
+data['p_sentiment_score']=p_sentiment_score
+
+data.to_csv('all_result1.csv')