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[text analytics] add sample stories and improve documents (#15429)
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iscai-msft authored Nov 18, 2020
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32 changes: 20 additions & 12 deletions sdk/textanalytics/azure-ai-textanalytics/README.md
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Expand Up @@ -147,7 +147,7 @@ The input for each operation is passed as a **list** of documents.

Each document can be passed as a string in the list, e.g.
```python
documents = ["I hated the movie. It was so slow!", "The movie made it into my top ten favorites.", "What a great movie!"]
documents = ["I hated the movie. It was so slow!", "The movie made it into my top ten favorites. What a great movie!"]
```

or, if you wish to pass in a per-item document `id` or `language`/`country_hint`, they can be passed as a list of
Expand All @@ -158,8 +158,7 @@ or a dict-like representation of the object:
```python
documents = [
{"id": "1", "language": "en", "text": "I hated the movie. It was so slow!"},
{"id": "2", "language": "en", "text": "The movie made it into my top ten favorites."},
{"id": "3", "language": "en", "text": "What a great movie!"}
{"id": "2", "language": "en", "text": "The movie made it into my top ten favorites. What a great movie!"},
]
```

Expand Down Expand Up @@ -210,7 +209,7 @@ endpoint="https://<region>.api.cognitive.microsoft.com/"
text_analytics_client = TextAnalyticsClient(endpoint, credential)

documents = [
"I did not like the restaurant. The food was too spicy.",
"I did not like the restaurant. The food was somehow both too spicy and underseasoned. Additionally, I thought the location was too far away from the playhouse.",
"The restaurant was decorated beautifully. The atmosphere was unlike any other restaurant I've been to.",
"The food was yummy. :)"
]
Expand Down Expand Up @@ -244,8 +243,10 @@ endpoint="https://<region>.api.cognitive.microsoft.com/"
text_analytics_client = TextAnalyticsClient(endpoint, credential)

documents = [
"Microsoft was founded by Bill Gates and Paul Allen.",
"Redmond is a city in King County, Washington, United States, located 15 miles east of Seattle.",
"""
Microsoft was founded by Bill Gates and Paul Allen. Its headquarters are located in Redmond. Redmond is a
city in King County, Washington, United States, located 15 miles east of Seattle.
""",
"Jeff bought three dozen eggs because there was a 50% discount."
]

Expand Down Expand Up @@ -280,7 +281,7 @@ endpoint="https://<region>.api.cognitive.microsoft.com/"
text_analytics_client = TextAnalyticsClient(endpoint, credential)

documents = [
"Microsoft was founded by Bill Gates and Paul Allen.",
"Microsoft was founded by Bill Gates and Paul Allen. Its headquarters are located in Redmond.",
"Easter Island, a Chilean territory, is a remote volcanic island in Polynesia."
]

Expand Down Expand Up @@ -318,8 +319,10 @@ endpoint="https://<region>.api.cognitive.microsoft.com/"
text_analytics_client = TextAnalyticsClient(endpoint, credential)

documents = [
"The employee's SSN is 859-98-0987.",
"The employee's phone number is 555-555-5555."
"""
We have an employee called Parker who cleans up after customers. The employee's
SSN is 859-98-0987, and their phone number is 555-555-5555.
"""
]
response = text_analytics_client.recognize_pii_entities(documents, language="en")
result = [doc for doc in response if not doc.is_error]
Expand Down Expand Up @@ -351,8 +354,10 @@ text_analytics_client = TextAnalyticsClient(endpoint, credential)

documents = [
"Redmond is a city in King County, Washington, United States, located 15 miles east of Seattle.",
"I need to take my cat to the veterinarian.",
"I will travel to South America in the summer."
"""
I need to take my cat to the veterinarian. He has been sick recently, and I need to take him
before I travel to South America for the summer.
""",
]

response = text_analytics_client.extract_key_phrases(documents, language="en")
Expand All @@ -379,7 +384,10 @@ endpoint="https://<region>.api.cognitive.microsoft.com/"
text_analytics_client = TextAnalyticsClient(endpoint, credential)

documents = [
"This is written in English.",
"""
This whole document is written in English. In order for the whole document to be written
in English, every sentence also has to be written in English, which it is.
""",
"Il documento scritto in italiano.",
"Dies ist in deutsche Sprache verfasst."
]
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8 changes: 4 additions & 4 deletions ...s/azure-ai-textanalytics/samples/async_samples/sample_alternative_document_input_async.py
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Expand Up @@ -37,11 +37,11 @@ async def alternative_document_input(self):
text_analytics_client = TextAnalyticsClient(endpoint=endpoint, credential=AzureKeyCredential(key))

documents = [
{"id": "0", "language": "en", "text": "I had the best day of my life."},
{"id": "1", "language": "en",
{"id": "0", "country_hint": "US", "text": "I had the best day of my life. I decided to go sky-diving and it made me appreciate my whole life so much more. I developed a deep-connection with my instructor as well."},
{"id": "1", "country_hint": "GB",
"text": "This was a waste of my time. The speaker put me to sleep."},
{"id": "2", "language": "es", "text": "No tengo dinero ni nada que dar..."},
{"id": "3", "language": "fr",
{"id": "2", "country_hint": "MX", "text": "No tengo dinero ni nada que dar..."},
{"id": "3", "country_hint": "FR",
"text": "L'hôtel n'était pas très confortable. L'éclairage était trop sombre."}
]
async with text_analytics_client:
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78 changes: 58 additions & 20 deletions ...tanalytics/azure-ai-textanalytics/samples/async_samples/sample_analyze_sentiment_async.py
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Expand Up @@ -13,6 +13,10 @@
This sample demonstrates how to analyze sentiment in documents.
An overall and per-sentence sentiment is returned.
In this sample we will be a skydiving company going through reviews people have left for our company.
We will extract the reviews that we are certain have a positive sentiment and post them onto our
website to attract more divers.
USAGE:
python sample_analyze_sentiment_async.py
Expand All @@ -28,6 +32,14 @@
class AnalyzeSentimentSampleAsync(object):

async def analyze_sentiment_async(self):
print(
"In this sample we will be combing through reviews customers have left about their"
"experience using our skydiving company, Contoso."
)
print(
"We start out with a list of reviews. Let us extract the reviews we are sure are "
"positive, so we can display them on our website and get even more customers!"
)
# [START analyze_sentiment_async]
from azure.core.credentials import AzureKeyCredential
from azure.ai.textanalytics.aio import TextAnalyticsClient
Expand All @@ -36,38 +48,64 @@ async def analyze_sentiment_async(self):
key = os.environ["AZURE_TEXT_ANALYTICS_KEY"]

text_analytics_client = TextAnalyticsClient(endpoint=endpoint, credential=AzureKeyCredential(key))

documents = [
"I had the best day of my life.",
"This was a waste of my time. The speaker put me to sleep.",
"No tengo dinero ni nada que dar...",
"L'hôtel n'était pas très confortable. L'éclairage était trop sombre."
"""I had the best day of my life. I decided to go sky-diving and it made me appreciate my whole life so much more.
I developed a deep-connection with my instructor as well, and I feel as if I've made a life-long friend in her.""",
"""This was a waste of my time. All of the views on this drop are extremely boring, all I saw was grass. 0/10 would
not recommend to any divers, even first timers.""",
"""This was pretty good! The sights were ok, and I had fun with my instructors! Can't complain too much about my experience""",
"""I only have one word for my experience: WOW!!! I can't believe I have had such a wonderful skydiving company right
in my backyard this whole time! I will definitely be a repeat customer, and I want to take my grandmother skydiving too,
I know she'll love it!"""
]

async with text_analytics_client:
result = await text_analytics_client.analyze_sentiment(documents)

docs = [doc for doc in result if not doc.is_error]

print("Let's visualize the sentiment of each of these documents")
for idx, doc in enumerate(docs):
print("Document text: {}".format(documents[idx]))
print("Overall sentiment: {}".format(doc.sentiment))
# [END analyze_sentiment_async]
print("Overall confidence scores: positive={}; neutral={}; negative={} \n".format(
doc.confidence_scores.positive,
doc.confidence_scores.neutral,
doc.confidence_scores.negative,
))
for sentence in doc.sentences:
print("Sentence '{}' has sentiment: {}".format(sentence.text, sentence.sentiment))
print("...Sentence is {} characters from the start of the document and is {} characters long".format(
sentence.offset, len(sentence.text)
))
print("...Sentence confidence scores: positive={}; neutral={}; negative={}".format(
sentence.confidence_scores.positive,
sentence.confidence_scores.neutral,
sentence.confidence_scores.negative,
))
print("------------------------------------")

print("Now, let us extract all of the positive reviews")
positive_reviews = [doc for doc in docs if doc.sentiment == 'positive']

print("We want to be very confident that our reviews are positive since we'll be posting them on our website.")
print("We're going to confirm our chosen reviews are positive using two different tests")

print(
"First, we are going to check how confident the sentiment analysis model is that a document is positive. "
"Let's go with a 90% confidence."
)
positive_reviews = [
review for review in positive_reviews
if review.confidence_scores.positive >= 0.9
]

print(
"Finally, we also want to make sure every sentence is positive so we only showcase our best selves!"
)
positive_reviews_final = []
for idx, review in enumerate(positive_reviews):
print("Looking at positive review #{}".format(idx + 1))
any_sentence_not_positive = False
for sentence in review.sentences:
print("...Sentence '{}' has sentiment '{}' with confidence scores '{}'".format(
sentence.text,
sentence.sentiment,
sentence.confidence_scores
)
)
if sentence.sentiment != 'positive':
any_sentence_not_positive = True
if not any_sentence_not_positive:
positive_reviews_final.append(review)

print("We now have the final list of positive reviews we are going to display on our website!")


async def main():
Expand Down
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