Azure 文本情感答案

【问题标题】：Azure Text SentimentAzure 文本情感
【发布时间】：2021-06-26 12:49:54
【问题描述】：

我正在尝试 azure 情感分析 api

from azure.core.credentials import AzureKeyCredential
from azure.ai.textanalytics import TextAnalyticsClient

credential = AzureKeyCredential("<api_key>")
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.",
    "The restaurant was decorated beautifully. The atmosphere was unlike any other restaurant I've been to.",
    "The food was yummy. :)"
]

response = text_analytics_client.analyze_sentiment(documents, language="en")
result = [doc for doc in response if not doc.is_error]

for doc in result:
    print("Overall sentiment: {}".format(doc.sentiment))
    print("Scores: positive={}; neutral={}; negative={} \n".format(
        doc.confidence_scores.positive,
        doc.confidence_scores.neutral,
        doc.confidence_scores.negative,
    ))

这段代码工作正常，但我想读取一个 df 列，获取文本情感并创建一个列来存储文本情感，你知道我该怎么做吗？我试图通过documents = df['column_name'] 和df[I]，但我得到了错误

【问题讨论】：

标签： python azure sentiment-analysis azure-cognitive-services

【解决方案1】：

以下代码应该为您提供一个包含两列的数据框，一列用于保存文档文本，另一列用于保存相应的情绪：

from azure.core.credentials import AzureKeyCredential
from azure.ai.textanalytics import TextAnalyticsClient
import pandas as pd
from tabulate import tabulate

documents = [
    "I did not like the restaurant. The food was too spicy.",
    "The restaurant was decorated beautifully. The atmosphere was unlike any other restaurant I've been to.",
    "The food was yummy. :)"
]

df = pd.DataFrame()
df["Documents"] = documents

response = text_analytics_client.analyze_sentiment(documents, language="en")
result = [doc for doc in response if not doc.is_error]

for doc in result:
    df["Sentiment"] = doc.sentiment
    print("Overall sentiment: {}".format(doc.sentiment))
    print("Scores: positive={}; neutral={}; negative={} \n".format(
        doc.confidence_scores.positive,
        doc.confidence_scores.neutral,
        doc.confidence_scores.negative,
    ))

print(tabulate(df, showindex=False, headers=df.columns))

输出：

Documents                                                                                               Sentiment
------------------------------------------------------------------------------------------------------  -----------
I did not like the restaurant. The food was too spicy.                                                  positive
The restaurant was decorated beautifully. The atmosphere was unlike any other restaurant I've been to.  positive
The food was yummy. :)                                                                                  positive

【讨论】：