[text analytics] Add text to sentence sentiment

sdk/textanalytics/azure-ai-textanalytics/CHANGELOG.md

@@ -2,6 +2,8 @@
 
 ## 1.0.0b5 (Unreleased)
 
+**New features**
+- Added `text` property to `SentenceSentiment`
 
 ## 1.0.0b4 (2020-04-07)
 

sdk/textanalytics/azure-ai-textanalytics/azure/ai/textanalytics/_models.py

@@ -597,6 +597,8 @@ class SentenceSentiment(DictMixin):
     """SentenceSentiment contains the predicted sentiment and
     confidence scores for each individual sentence in the document.
 
+    :param text: The sentence text.
+    :type text: str
     :param sentiment: The predicted Sentiment for the sentence.
         Possible values include: 'positive', 'neutral', 'negative'
     :type sentiment: str
@@ -612,6 +614,7 @@ class SentenceSentiment(DictMixin):
     """
 
     def __init__(self, **kwargs):
+        self.text = kwargs.get("text", None)
         self.sentiment = kwargs.get("sentiment", None)
         self.confidence_scores = kwargs.get("confidence_scores", None)
         self.grapheme_offset = kwargs.get("grapheme_offset", None)
@@ -620,15 +623,17 @@ def __init__(self, **kwargs):
     @classmethod
     def _from_generated(cls, sentence):
         return cls(
+            text=sentence.text,
             sentiment=sentence.sentiment,
             confidence_scores=SentimentConfidenceScores._from_generated(sentence.confidence_scores),  # pylint: disable=protected-access
             grapheme_offset=sentence.offset,
             grapheme_length=sentence.length
         )
 
     def __repr__(self):
-        return "SentenceSentiment(sentiment={}, confidence_scores={}, grapheme_offset={}, grapheme_length={})".format(
-            self.sentiment, repr(self.confidence_scores), self.grapheme_offset, self.grapheme_length
+        return "SentenceSentiment(text={}, sentiment={}, confidence_scores={}, grapheme_offset={}, "\
+            "grapheme_length={})".format(self.text, self.sentiment, repr(self.confidence_scores),
+            self.grapheme_offset, self.grapheme_length
         )[:1024]
 
 

sdk/textanalytics/azure-ai-textanalytics/samples/async_samples/sample_analyze_sentiment_async.py

@@ -56,8 +56,8 @@ async def analyze_sentiment_async(self):
                 doc.confidence_scores.neutral,
                 doc.confidence_scores.negative,
             ))
-            for idx, sentence in enumerate(doc.sentences):
-                print("Sentence {} sentiment: {}".format(idx+1, sentence.sentiment))
+            for sentence in doc.sentences:
+                print("Sentence '{}' has sentiment: {}".format(sentence.text, sentence.sentiment))
                 print("Sentence confidence scores: positive={}; neutral={}; negative={}".format(
                     sentence.confidence_scores.positive,
                     sentence.confidence_scores.neutral,

sdk/textanalytics/azure-ai-textanalytics/samples/sample_analyze_sentiment.py

@@ -53,8 +53,8 @@ def analyze_sentiment(self):
                 doc.confidence_scores.neutral,
                 doc.confidence_scores.negative,
             ))
-            for idx, sentence in enumerate(doc.sentences):
-                print("Sentence {} sentiment: {}".format(idx+1, sentence.sentiment))
+            for sentence in doc.sentences:
+                print("Sentence '{}' has sentiment: {}".format(sentence.text, sentence.sentiment))
                 print("Sentence confidence scores: positive={}; neutral={}; negative={}".format(
                     sentence.confidence_scores.positive,
                     sentence.confidence_scores.neutral,

sdk/textanalytics/azure-ai-textanalytics/tests/test_analyze_sentiment.py

@@ -47,6 +47,15 @@ def test_all_successful_passing_dict(self, client):
             self.assertIsNotNone(doc.confidence_scores)
             self.assertIsNotNone(doc.sentences)
 
+        self.assertEqual(len(response[0].sentences), 1)
+        self.assertEqual(response[0].sentences[0].text, "Microsoft was founded by Bill Gates and Paul Allen.")
+        self.assertEqual(len(response[1].sentences), 2)
+        self.assertEqual(response[1].sentences[0].text, "I did not like the hotel we stayed at.")
+        self.assertEqual(response[1].sentences[1].text, "It was too expensive.")
+        self.assertEqual(len(response[2].sentences), 2)
+        self.assertEqual(response[2].sentences[0].text, "The restaurant had really good food.")
+        self.assertEqual(response[2].sentences[1].text, "I recommend you try it.")
+
     @GlobalTextAnalyticsAccountPreparer()
     @TextAnalyticsClientPreparer()
     def test_all_successful_passing_text_document_input(self, client):
@@ -65,6 +74,15 @@ def test_all_successful_passing_text_document_input(self, client):
             self.assertIsNotNone(doc.confidence_scores)
             self.assertIsNotNone(doc.sentences)
 
+        self.assertEqual(len(response[0].sentences), 1)
+        self.assertEqual(response[0].sentences[0].text, "Microsoft was founded by Bill Gates and Paul Allen.")
+        self.assertEqual(len(response[1].sentences), 2)
+        self.assertEqual(response[1].sentences[0].text, "I did not like the hotel we stayed at.")
+        self.assertEqual(response[1].sentences[1].text, "It was too expensive.")
+        self.assertEqual(len(response[2].sentences), 2)
+        self.assertEqual(response[2].sentences[0].text, "The restaurant had really good food.")
+        self.assertEqual(response[2].sentences[1].text, "I recommend you try it.")
+
     @GlobalTextAnalyticsAccountPreparer()
     @TextAnalyticsClientPreparer()
     def test_passing_only_string(self, client):

sdk/textanalytics/azure-ai-textanalytics/tests/test_analyze_sentiment_async.py

@@ -63,6 +63,15 @@ async def test_all_successful_passing_dict(self, client):
             self.assertIsNotNone(doc.confidence_scores)
             self.assertIsNotNone(doc.sentences)
 
+        self.assertEqual(len(response[0].sentences), 1)
+        self.assertEqual(response[0].sentences[0].text, "Microsoft was founded by Bill Gates and Paul Allen.")
+        self.assertEqual(len(response[1].sentences), 2)
+        self.assertEqual(response[1].sentences[0].text, "I did not like the hotel we stayed at.")
+        self.assertEqual(response[1].sentences[1].text, "It was too expensive.")
+        self.assertEqual(len(response[2].sentences), 2)
+        self.assertEqual(response[2].sentences[0].text, "The restaurant had really good food.")
+        self.assertEqual(response[2].sentences[1].text, "I recommend you try it.")
+
     @GlobalTextAnalyticsAccountPreparer()
     @TextAnalyticsClientPreparer()
     async def test_all_successful_passing_text_document_input(self, client):
@@ -81,6 +90,15 @@ async def test_all_successful_passing_text_document_input(self, client):
             self.assertIsNotNone(doc.confidence_scores)
             self.assertIsNotNone(doc.sentences)
 
+        self.assertEqual(len(response[0].sentences), 1)
+        self.assertEqual(response[0].sentences[0].text, "Microsoft was founded by Bill Gates and Paul Allen.")
+        self.assertEqual(len(response[1].sentences), 2)
+        self.assertEqual(response[1].sentences[0].text, "I did not like the hotel we stayed at.")
+        self.assertEqual(response[1].sentences[1].text, "It was too expensive.")
+        self.assertEqual(len(response[2].sentences), 2)
+        self.assertEqual(response[2].sentences[0].text, "The restaurant had really good food.")
+        self.assertEqual(response[2].sentences[1].text, "I recommend you try it.")
+
     @GlobalTextAnalyticsAccountPreparer()
     @TextAnalyticsClientPreparer()
     async def test_passing_only_string(self, client):

sdk/textanalytics/azure-ai-textanalytics/tests/test_text_analytics.py

@@ -79,6 +79,7 @@ def test_repr(self):
             _models.SentimentConfidenceScores(positive=0.99, neutral=0.05, negative=0.02)
 
         sentence_sentiment = _models.SentenceSentiment(
+            text="This is a sentence.",
             sentiment="neutral",
             confidence_scores=sentiment_confidence_score_per_label,
             grapheme_offset=0,
@@ -141,12 +142,12 @@ def test_repr(self):
                          "transaction_count=18), is_error=False)", repr(recognize_linked_entities_result))
         self.assertEqual("SentimentConfidenceScores(positive=0.99, neutral=0.05, negative=0.02)",
                          repr(sentiment_confidence_score_per_label))
-        self.assertEqual("SentenceSentiment(sentiment=neutral, confidence_scores=SentimentConfidenceScores("
+        self.assertEqual("SentenceSentiment(text=This is a sentence., sentiment=neutral, confidence_scores=SentimentConfidenceScores("
                          "positive=0.99, neutral=0.05, negative=0.02), grapheme_offset=0, grapheme_length=10)", repr(sentence_sentiment))
         self.assertEqual("AnalyzeSentimentResult(id=1, sentiment=positive, statistics=TextDocumentStatistics("
                          "grapheme_count=14, transaction_count=18), confidence_scores=SentimentConfidenceScores"
                          "(positive=0.99, neutral=0.05, negative=0.02), "
-                         "sentences=[SentenceSentiment(sentiment=neutral, confidence_scores="
+                         "sentences=[SentenceSentiment(text=This is a sentence., sentiment=neutral, confidence_scores="
                          "SentimentConfidenceScores(positive=0.99, neutral=0.05, negative=0.02), "
                          "grapheme_offset=0, grapheme_length=10)], is_error=False)",
                          repr(analyze_sentiment_result))