将 Fabric 中预生成的文本分析与 REST API 和 SynapseML 结合使用（预览版）
重要
此功能目前为预览版。
文本分析 是一种 Azure AI 服务，可用于使用自然语言处理（NLP）功能执行文本挖掘和文本分析。
本教程演示如何将 Fabric 中的文本分析与 RESTful API 配合使用以：
- 检测句子或文档级别的情绪标签。
- 标识给定文本输入的语言。
- 从文本中提取关键阶段。
- 识别文本中的不同实体，并将其分类为预定义类或类型。
先决条件
# Get workload endpoints and access token
from synapse.ml.mlflow import get_mlflow_env_config
import json
mlflow_env_configs = get_mlflow_env_config()
access_token = access_token = mlflow_env_configs.driver_aad_token
prebuilt_AI_base_host = mlflow_env_configs.workload_endpoint + "cognitive/textanalytics/"
print("Workload endpoint for AI service: \n" + prebuilt_AI_base_host)
service_url = prebuilt_AI_base_host + "language/:analyze-text?api-version=2022-05-01"
# Make a RESful request to AI service
post_headers = {
"Content-Type" : "application/json",
"Authorization" : "Bearer {}".format(access_token)
}
def printresponse(response):
print(f"HTTP {response.status_code}")
if response.status_code == 200:
try:
result = response.json()
print(json.dumps(result, indent=2, ensure_ascii=False))
except:
print(f"pasre error {response.content}")
else:
print(response.headers)
print(f"error message: {response.content}")
情绪分析
情绪分析功能提供了一种检测情绪标签（如“负”、“中性”和“积极”）以及句子和文档级别的置信度分数的方法。 此功能还为每个文档及其内部的句子返回介于 0 和 1 之间的置信度分数，评估积极、中立和负面情绪。 请参阅 情绪分析和观点挖掘语言支持，以获取启用的语言列表。
import requests
from pprint import pprint
import uuid
post_body = {
"kind": "SentimentAnalysis",
"parameters": {
"modelVersion": "latest",
"opinionMining": "True"
},
"analysisInput":{
"documents":[
{
"id":"1",
"language":"en",
"text": "The food and service were unacceptable. The concierge was nice, however."
}
]
}
}
post_headers["x-ms-workload-resource-moniker"] = str(uuid.uuid1())
response = requests.post(service_url, json=post_body, headers=post_headers)
# Output all information of the request process
printresponse(response)
输出
HTTP 200
{
"kind": "SentimentAnalysisResults",
"results": {
"documents": [
{
"id": "1",
"sentiment": "mixed",
"confidenceScores": {
"positive": 0.43,
"neutral": 0.04,
"negative": 0.53
},
"sentences": [
{
"sentiment": "negative",
"confidenceScores": {
"positive": 0.0,
"neutral": 0.01,
"negative": 0.99
},
"offset": 0,
"length": 40,
"text": "The food and service were unacceptable. ",
"targets": [
{
"sentiment": "negative",
"confidenceScores": {
"positive": 0.01,
"negative": 0.99
},
"offset": 4,
"length": 4,
"text": "food",
"relations": [
{
"relationType": "assessment",
"ref": "#/documents/0/sentences/0/assessments/0"
}
]
},
{
"sentiment": "negative",
"confidenceScores": {
"positive": 0.01,
"negative": 0.99
},
"offset": 13,
"length": 7,
"text": "service",
"relations": [
{
"relationType": "assessment",
"ref": "#/documents/0/sentences/0/assessments/0"
}
]
}
],
"assessments": [
{
"sentiment": "negative",
"confidenceScores": {
"positive": 0.01,
"negative": 0.99
},
"offset": 26,
"length": 12,
"text": "unacceptable",
"isNegated": false
}
]
},
{
"sentiment": "positive",
"confidenceScores": {
"positive": 0.86,
"neutral": 0.08,
"negative": 0.07
},
"offset": 40,
"length": 32,
"text": "The concierge was nice, however.",
"targets": [
{
"sentiment": "positive",
"confidenceScores": {
"positive": 1.0,
"negative": 0.0
},
"offset": 44,
"length": 9,
"text": "concierge",
"relations": [
{
"relationType": "assessment",
"ref": "#/documents/0/sentences/1/assessments/0"
}
]
}
],
"assessments": [
{
"sentiment": "positive",
"confidenceScores": {
"positive": 1.0,
"negative": 0.0
},
"offset": 58,
"length": 4,
"text": "nice",
"isNegated": false
}
]
}
],
"warnings": []
}
],
"errors": [],
"modelVersion": "2022-11-01"
}
}
语言检测器
语言检测器评估每个文档的文本输入，并返回具有指示分析强度的分数的语言标识符。 此功能对于收集任意文本的内容存储非常有用，其中语言未知。 请参阅语言检测支持的语言以获取支持的语言列表。
post_body = {
"kind": "LanguageDetection",
"parameters": {
"modelVersion": "latest"
},
"analysisInput":{
"documents":[
{
"id":"1",
"text": "This is a document written in English."
}
]
}
}
post_headers["x-ms-workload-resource-moniker"] = str(uuid.uuid1())
response = requests.post(service_url, json=post_body, headers=post_headers)
# Output all information of the request process
printresponse(response)
输出
HTTP 200
{
"kind": "LanguageDetectionResults",
"results": {
"documents": [
{
"id": "1",
"detectedLanguage": {
"name": "English",
"iso6391Name": "en",
"confidenceScore": 0.99
},
"warnings": []
}
],
"errors": [],
"modelVersion": "2022-10-01"
}
}
关键短语提取程序
关键短语提取计算非结构化文本并返回关键短语的列表。 如果需要快速识别文档集合中的要点，此功能非常有用。 请参阅关键短语提取支持的语言以获取支持的语言列表。
post_body = {
"kind": "KeyPhraseExtraction",
"parameters": {
"modelVersion": "latest"
},
"analysisInput":{
"documents":[
{
"id":"1",
"language":"en",
"text": "Dr. Smith has a very modern medical office, and she has great staff."
}
]
}
}
post_headers["x-ms-workload-resource-moniker"] = str(uuid.uuid1())
response = requests.post(service_url, json=post_body, headers=post_headers)
# Output all information of the request process
printresponse(response)
输出
HTTP 200
{
"kind": "KeyPhraseExtractionResults",
"results": {
"documents": [
{
"id": "1",
"keyPhrases": [
"modern medical office",
"Dr. Smith",
"great staff"
],
"warnings": []
}
],
"errors": [],
"modelVersion": "2022-10-01"
}
}
命名实体识别 (NER)
命名实体识别（NER）能够识别文本中的不同实体，并将其分类为预定义类或类型，例如：人员、位置、事件、产品和组织。 有关已启用的语言列表，请参阅 NER 语言支持。
post_body = {
"kind": "EntityRecognition",
"parameters": {
"modelVersion": "latest"
},
"analysisInput":{
"documents":[
{
"id":"1",
"language": "en",
"text": "I had a wonderful trip to Seattle last week."
}
]
}
}
post_headers["x-ms-workload-resource-moniker"] = str(uuid.uuid1())
response = requests.post(service_url, json=post_body, headers=post_headers)
# Output all information of the request process
printresponse(response)
输出
HTTP 200
{
"kind": "EntityRecognitionResults",
"results": {
"documents": [
{
"id": "1",
"entities": [
{
"text": "trip",
"category": "Event",
"offset": 18,
"length": 4,
"confidenceScore": 0.74
},
{
"text": "Seattle",
"category": "Location",
"subcategory": "GPE",
"offset": 26,
"length": 7,
"confidenceScore": 1.0
},
{
"text": "last week",
"category": "DateTime",
"subcategory": "DateRange",
"offset": 34,
"length": 9,
"confidenceScore": 0.8
}
],
"warnings": []
}
],
"errors": [],
"modelVersion": "2021-06-01"
}
}