本文為英文版的機器翻譯版本，如內容有任何歧義或不一致之處，概以英文版為準。

# 使用 Amazon Comprehend 和 AWS SDK 偵測文件元素
<a name="example_comprehend_Usage_DetectApis_section"></a>

以下程式碼範例顯示做法：
+ 偵測文件中的語言、實體和關鍵片語。
+ 偵測文件中的個人身分識別資訊 (PII)。
+ 偵測文件的情緒。
+ 偵測文件中的語法元素。

------
#### [ Python ]

**適用於 Python 的 SDK (Boto3)**  
 GitHub 上提供更多範例。尋找完整範例，並了解如何在 [AWS 程式碼範例儲存庫](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples)中設定和執行。
建立包裝 Amazon Comprehend 動作的類別。  

```
import logging
from pprint import pprint
import boto3
from botocore.exceptions import ClientError

logger = logging.getLogger(__name__)

class ComprehendDetect:
    """Encapsulates Comprehend detection functions."""

    def __init__(self, comprehend_client):
        """
        :param comprehend_client: A Boto3 Comprehend client.
        """
        self.comprehend_client = comprehend_client


    def detect_languages(self, text):
        """
        Detects languages used in a document.

        :param text: The document to inspect.
        :return: The list of languages along with their confidence scores.
        """
        try:
            response = self.comprehend_client.detect_dominant_language(Text=text)
            languages = response["Languages"]
            logger.info("Detected %s languages.", len(languages))
        except ClientError:
            logger.exception("Couldn't detect languages.")
            raise
        else:
            return languages


    def detect_entities(self, text, language_code):
        """
        Detects entities in a document. Entities can be things like people and places
        or other common terms.

        :param text: The document to inspect.
        :param language_code: The language of the document.
        :return: The list of entities along with their confidence scores.
        """
        try:
            response = self.comprehend_client.detect_entities(
                Text=text, LanguageCode=language_code
            )
            entities = response["Entities"]
            logger.info("Detected %s entities.", len(entities))
        except ClientError:
            logger.exception("Couldn't detect entities.")
            raise
        else:
            return entities


    def detect_key_phrases(self, text, language_code):
        """
        Detects key phrases in a document. A key phrase is typically a noun and its
        modifiers.

        :param text: The document to inspect.
        :param language_code: The language of the document.
        :return: The list of key phrases along with their confidence scores.
        """
        try:
            response = self.comprehend_client.detect_key_phrases(
                Text=text, LanguageCode=language_code
            )
            phrases = response["KeyPhrases"]
            logger.info("Detected %s phrases.", len(phrases))
        except ClientError:
            logger.exception("Couldn't detect phrases.")
            raise
        else:
            return phrases


    def detect_pii(self, text, language_code):
        """
        Detects personally identifiable information (PII) in a document. PII can be
        things like names, account numbers, or addresses.

        :param text: The document to inspect.
        :param language_code: The language of the document.
        :return: The list of PII entities along with their confidence scores.
        """
        try:
            response = self.comprehend_client.detect_pii_entities(
                Text=text, LanguageCode=language_code
            )
            entities = response["Entities"]
            logger.info("Detected %s PII entities.", len(entities))
        except ClientError:
            logger.exception("Couldn't detect PII entities.")
            raise
        else:
            return entities


    def detect_sentiment(self, text, language_code):
        """
        Detects the overall sentiment expressed in a document. Sentiment can
        be positive, negative, neutral, or a mixture.

        :param text: The document to inspect.
        :param language_code: The language of the document.
        :return: The sentiments along with their confidence scores.
        """
        try:
            response = self.comprehend_client.detect_sentiment(
                Text=text, LanguageCode=language_code
            )
            logger.info("Detected primary sentiment %s.", response["Sentiment"])
        except ClientError:
            logger.exception("Couldn't detect sentiment.")
            raise
        else:
            return response


    def detect_syntax(self, text, language_code):
        """
        Detects syntactical elements of a document. Syntax tokens are portions of
        text along with their use as parts of speech, such as nouns, verbs, and
        interjections.

        :param text: The document to inspect.
        :param language_code: The language of the document.
        :return: The list of syntax tokens along with their confidence scores.
        """
        try:
            response = self.comprehend_client.detect_syntax(
                Text=text, LanguageCode=language_code
            )
            tokens = response["SyntaxTokens"]
            logger.info("Detected %s syntax tokens.", len(tokens))
        except ClientError:
            logger.exception("Couldn't detect syntax.")
            raise
        else:
            return tokens
```
呼叫在包裝函式類別上的函數，以偵測文件中的實體、片語等。  

```
def usage_demo():
    print("-" * 88)
    print("Welcome to the Amazon Comprehend detection demo!")
    print("-" * 88)

    logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s")

    comp_detect = ComprehendDetect(boto3.client("comprehend"))
    with open("detect_sample.txt") as sample_file:
        sample_text = sample_file.read()

    demo_size = 3

    print("Sample text used for this demo:")
    print("-" * 88)
    print(sample_text)
    print("-" * 88)

    print("Detecting languages.")
    languages = comp_detect.detect_languages(sample_text)
    pprint(languages)
    lang_code = languages[0]["LanguageCode"]

    print("Detecting entities.")
    entities = comp_detect.detect_entities(sample_text, lang_code)
    print(f"The first {demo_size} are:")
    pprint(entities[:demo_size])

    print("Detecting key phrases.")
    phrases = comp_detect.detect_key_phrases(sample_text, lang_code)
    print(f"The first {demo_size} are:")
    pprint(phrases[:demo_size])

    print("Detecting personally identifiable information (PII).")
    pii_entities = comp_detect.detect_pii(sample_text, lang_code)
    print(f"The first {demo_size} are:")
    pprint(pii_entities[:demo_size])

    print("Detecting sentiment.")
    sentiment = comp_detect.detect_sentiment(sample_text, lang_code)
    print(f"Sentiment: {sentiment['Sentiment']}")
    print("SentimentScore:")
    pprint(sentiment["SentimentScore"])

    print("Detecting syntax elements.")
    syntax_tokens = comp_detect.detect_syntax(sample_text, lang_code)
    print(f"The first {demo_size} are:")
    pprint(syntax_tokens[:demo_size])

    print("Thanks for watching!")
    print("-" * 88)
```
+ 如需 API 詳細資訊，請參閱《適用於 Python (Boto3) 的AWS SDK API 參考》**中的下列主題。
  + [DetectDominantLanguage](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/DetectDominantLanguage)
  + [DetectEntities](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/DetectEntities)
  + [DetectKeyPhrases](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/DetectKeyPhrases)
  + [DetectPiiEntities](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/DetectPiiEntities)
  + [DetectSentiment](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/DetectSentiment)
  + [DetectSyntax](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/DetectSyntax)

------

如需 AWS SDK 開發人員指南和程式碼範例的完整清單，請參閱 [搭配 SDK 使用 Amazon Comprehend AWS](sdk-general-information-section.md)。此主題也包含有關入門的資訊和舊版 SDK 的詳細資訊。