Refactored the Sentiment Analysis tutorial to use the Cloud Client Lib

language/sentiment/requirements.txt

@@ -1 +1 @@
-google-api-python-client==1.5.5
+google-cloud-language==0.22.2

language/sentiment/sentiment_analysis.py

@@ -11,38 +11,24 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+# [START sentiment_tutorial]
 """Demonstrates how to make a simple call to the Natural Language API."""
 
+# [START sentiment_tutorial_import]
 import argparse
 
-from googleapiclient import discovery
-from oauth2client.client import GoogleCredentials
+from google.cloud import language
+# [END sentiment_tutorial_import]
 
 
-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)
+def print_result(annotations):
+    score = annotations.sentiment.score
+    magnitude = annotations.sentiment.magnitude
 
-    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()
-
-    score = response['documentSentiment']['score']
-    magnitude = response['documentSentiment']['magnitude']
-
-    for i, sentence in enumerate(response['sentences']):
-        sentence_sentiment = sentence['sentiment']['score']
+    for index, sentence in enumerate(annotations.sentences):
+        sentence_sentiment = sentence.sentiment.score
         print('Sentence {} has a sentiment score of {}'.format(
-            i, sentence_sentiment))
+            index, sentence_sentiment))
 
     print('Overall Sentiment: score of {} with magnitude of {}'.format(
         score, magnitude))
@@ -53,6 +39,23 @@ def main(movie_review_filename):
     return 0
 
 
+def analyze(movie_review_filename):
+    """Run a sentiment analysis request on text within a passed filename."""
+    language_client = language.Client()
+
+    with open(movie_review_filename, 'r') as review_file:
+        # 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)
+
+        # Print the results
+        print_result(annotations)
+
+
 if __name__ == '__main__':
     parser = argparse.ArgumentParser(
         description=__doc__,
@@ -61,4 +64,6 @@ def main(movie_review_filename):
         'movie_review_filename',
         help='The filename of the movie review you\'d like to analyze.')
     args = parser.parse_args()
-    main(args.movie_review_filename)
+
+    analyze(args.movie_review_filename)
+# [END sentiment_tutorial]

language/sentiment/sentiment_analysis_test.py

@@ -13,35 +13,35 @@
 
 import re
 
-from sentiment_analysis import main
+from sentiment_analysis import analyze
 
 
 def test_pos(resource, capsys):
-    main(resource('pos.txt'))
+    analyze(resource('pos.txt'))
     out, err = capsys.readouterr()
     score = float(re.search('score of (.+?) with', out).group(1))
     magnitude = float(re.search('magnitude of (.+?)', out).group(1))
     assert score * magnitude > 0
 
 
 def test_neg(resource, capsys):
-    main(resource('neg.txt'))
+    analyze(resource('neg.txt'))
     out, err = capsys.readouterr()
     score = float(re.search('score of (.+?) with', out).group(1))
     magnitude = float(re.search('magnitude of (.+?)', out).group(1))
     assert score * magnitude < 0
 
 
 def test_mixed(resource, capsys):
-    main(resource('mixed.txt'))
+    analyze(resource('mixed.txt'))
     out, err = capsys.readouterr()
     score = float(re.search('score of (.+?) with', out).group(1))
     assert score <= 0.3
     assert score >= -0.3
 
 
 def test_neutral(resource, capsys):
-    main(resource('neutral.txt'))
+    analyze(resource('neutral.txt'))
     out, err = capsys.readouterr()
     magnitude = float(re.search('magnitude of (.+?)', out).group(1))
     assert magnitude <= 2.0