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# coding=utf-8
# ------------------------------------
# Copyright (c) Microsoft.
# Licensed under the MIT License.
# ------------------------------------
"""
FILE: sample_analyze_sentiment.py
DESCRIPTION:
This sample demonstrates how to run **sentiment analysis** over text.
USAGE:
python sample_analyze_sentiment.py
REQUIRED ENV VARS (for AAD / DefaultAzureCredential):
AZURE_TEXT_ENDPOINT
AZURE_CLIENT_ID
AZURE_TENANT_ID
AZURE_CLIENT_SECRET
NOTE:
If you want to use AzureKeyCredential instead, set:
- AZURE_TEXT_ENDPOINT
- AZURE_TEXT_KEY
"""
# [START analyze_sentiment]
import os
from azure.identity import DefaultAzureCredential
from azure.ai.textanalytics import TextAnalysisClient
from azure.ai.textanalytics.models import (
MultiLanguageTextInput,
MultiLanguageInput,
TextSentimentAnalysisInput,
AnalyzeTextSentimentResult,
)
def sample_analyze_sentiment():
# settings
endpoint = os.environ["AZURE_TEXT_ENDPOINT"]
credential = DefaultAzureCredential()
client = TextAnalysisClient(endpoint, credential=credential)
# input
text_a = (
"The food and service were unacceptable, but the concierge were nice. "
"After talking to them about the quality of the food and the process to get room service "
"they refunded the money we spent at the restaurant and gave us a voucher for nearby restaurants."
)
body = TextSentimentAnalysisInput(
text_input=MultiLanguageTextInput(
multi_language_inputs=[MultiLanguageInput(id="A", text=text_a, language="en")]
)
)
# Sync (non-LRO) call
result = client.analyze_text(body=body)
# Print results
if isinstance(result, AnalyzeTextSentimentResult) and result.results and result.results.documents:
for doc in result.results.documents:
print(f"\nDocument ID: {doc.id}")
print(f"Overall sentiment: {doc.sentiment}")
if doc.confidence_scores:
print("Confidence scores:")
print(f" positive={doc.confidence_scores.positive}")
print(f" neutral={doc.confidence_scores.neutral}")
print(f" negative={doc.confidence_scores.negative}")
if doc.sentences:
print("\nSentence sentiments:")
for s in doc.sentences:
print(f" Text: {s.text}")
print(f" Sentiment: {s.sentiment}")
if s.confidence_scores:
print(
" Scores: "
f"pos={s.confidence_scores.positive}, "
f"neu={s.confidence_scores.neutral}, "
f"neg={s.confidence_scores.negative}"
)
print(f" Offset: {s.offset}, Length: {s.length}\n")
else:
print("No sentence-level results returned.")
else:
print("No documents in the response or unexpected result type.")
# [END analyze_sentiment]
def main():
sample_analyze_sentiment()
if __name__ == "__main__":
main()
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