Refactored the Sentiment Analysis tutorial to use the Cloud Client Library.

Author: jmdobry

language/sentiment/requirements.txt

@@ -1 +1 @@
-google-api-python-client==1.5.5
+google-cloud-language==0.22.0

language/sentiment/sentiment_analysis.py

@@ -15,32 +15,26 @@
 
 import argparse
 
-from googleapiclient import discovery
-from oauth2client.client import GoogleCredentials
+from google.cloud import language
 
 
 def main(movie_review_filename):
     """Run a sentiment analysis request on text within a passed filename."""
-
-    credentials = GoogleCredentials.get_application_default()
-    service = discovery.build('language', 'v1', credentials=credentials)
+    language_client = language.Client()
 
     with open(movie_review_filename, 'r') as review_file:
-        service_request = service.documents().analyzeSentiment(
-            body={
-                'document': {
-                    'type': 'PLAIN_TEXT',
-                    'content': review_file.read(),
-                }
-            }
-        )
-        response = service_request.execute()
+        # Instantiates a plain text document.
+        document = language_client.document_from_html(review_file.read())
+
+        # Detects sentiment in the document.
+        annotations = document.annotate_text(include_sentiment=True,
+            include_syntax=False, include_entities=False)
 
-    score = response['documentSentiment']['score']
-    magnitude = response['documentSentiment']['magnitude']
+    score = annotations.sentiment.score
+    magnitude = annotations.sentiment.magnitude
 
-    for i, sentence in enumerate(response['sentences']):
-        sentence_sentiment = sentence['sentiment']['score']
+    for i, sentence in enumerate(annotations.sentences):
+        sentence_sentiment = sentence.sentiment.score
         print('Sentence {} has a sentiment score of {}'.format(
             i, sentence_sentiment))