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# coding=utf-8
# ------------------------------------
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
# ------------------------------------
"""
FILE: sample_conv_sentiment.py
DESCRIPTION:
This sample demonstrates how to analyze a conversation for sentiment analysis.
For more info about how to setup a CLU conversation project, see the README.
USAGE:
python sample_conv_sentiment.py
Set the environment variables with your own values before running the sample:
1) AZURE_CONVERSATIONS_ENDPOINT - endpoint for your CLU resource.
2) AZURE_CONVERSATIONS_KEY - API key for your CLU resource.
"""
def sample_conv_sentiment():
# import libraries
import os
from azure.core.credentials import AzureKeyCredential
from azure.ai.language.conversations import ConversationAnalysisClient
# get secrets
endpoint = os.environ["AZURE_CONVERSATIONS_ENDPOINT"]
key = os.environ["AZURE_CONVERSATIONS_KEY"]
# analyze query
client = ConversationAnalysisClient(endpoint, AzureKeyCredential(key))
with client:
poller = client.begin_conversation_analysis(
task={
"displayName": "Analyze sentiment in conversation",
"analysisInput": {
"conversations": [
{
"conversationItems": [
{
"id": "1",
"participantId": "Customer",
"modality": "transcript",
"text": "My cat doesn't like the food, Can I get a refund?",
"lexical": "my cat doesn't like the food can i get a refund",
},
{
"id": "2",
"participantId": "Agent",
"modality": "transcript",
"text": "Sure. You have been refunded. Have a nice day.",
"lexical": "sure you have been refunded have a nice day",
},
{
"id": "3",
"participantId": "Customer",
"modality": "transcript",
"text": "Thanks for your help",
"lexical": "thanks for your help",
},
],
"modality": "transcript",
"id": "conversation1",
"language": "en",
}
]
},
"tasks": [
{
"kind": "ConversationalSentimentTask",
"parameters": {
"modelVersion": "2022-10-01-preview",
"predictionSource": "text",
},
}
],
}
)
# view result
result = poller.result()
task_result = result["tasks"]["items"][0]
print("... view task status ...")
print(f"status: {task_result['status']}")
conv_sentiment_result = task_result["results"]
if conv_sentiment_result["errors"]:
print("... errors occurred ...")
for error in conv_sentiment_result["errors"]:
print(error)
else:
conversation_result = conv_sentiment_result["conversations"][0]
if conversation_result["warnings"]:
print("... view warnings ...")
for warning in conversation_result["warnings"]:
print(warning)
else:
print("... view task result ...")
for conversation_item in conversation_result["conversationItems"]:
confidence_score = conversation_item["confidenceScores"]
print(f"id: {conversation_item['id']}")
print(f"participantId: {conversation_item['participantId']}")
print(f"sentiment: {conversation_item['sentiment']}")
print(
f"confidenceScores: positive={confidence_score['positive']} "
f"neutral={confidence_score['neutral']} negative={confidence_score['negative']}"
)
if __name__ == "__main__":
sample_conv_sentiment()
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