gcp natural language api japanese test

gcp-nlp-api-jp-koubun.py

from google.cloud import language_v1

def sample_analyze_syntax(text_content):
    client = language_v1.LanguageServiceClient()
    type_ = language_v1.Document.Type.PLAIN_TEXT
    language = "ja"
    document = {"content": text_content, "type_": type_, "language": language}
    encoding_type = language_v1.EncodingType.UTF8
    response = client.analyze_syntax(request = {'document': document, 'encoding_type': encoding_type})
    print(u"Language of the text: {}".format(response.language))
    for token in response.tokens:
        text = token.text
        print(u"Token text: {}".format(text.content))
        print(
            u"Location of this token in overall document: {}".format(text.begin_offset)
        )
        part_of_speech = token.part_of_speech
        print(
            u"Part of Speech tag: {}".format(
                language_v1.PartOfSpeech.Tag(part_of_speech.tag).name
            )
        )
        # print(u"Voice: {}".format(language_v1.PartOfSpeech.Voice(part_of_speech.voice).name))
        # print(u"Tense: {}".format(language_v1.PartOfSpeech.Tense(part_of_speech.tense).name))
        # print(u"Lemma: {}".format(token.lemma))
        dependency_edge = token.dependency_edge
        print(u"Head token index: {}".format(dependency_edge.head_token_index))
        print(
            u"Label: {}".format(language_v1.DependencyEdge.Label(dependency_edge.label).name)
        )
        print()


text = """ウクライナのゼレンスキー大統領は、５日に国連安全保障理事会で演説すると表明した。首都キーウ（キエフ）近郊で多数の民間人の遺体が見つかった問題について、他の地域でさらに多くの民間人がロシア軍に殺害された可能性があるとの見方を示した。"""
sample_analyze_syntax(text)

gcp-nlp-api-jp-エンティティ分析.py

from google.cloud import language_v1

def analyze_entities(text_content):
    """
    Analyzing Entities in a String

    Args:
      text_content The text content to analyze
    """

    client = language_v1.LanguageServiceClient()
    type_ = language_v1.Document.Type.PLAIN_TEXT
    language = "ja"
    document = {"content": text_content, "type_": type_, "language": language}
    encoding_type = language_v1.EncodingType.UTF8
    response = client.analyze_entities(request = {'document': document, 'encoding_type': encoding_type})
    for entity in response.entities:
        print(u"Representative name for the entity: {}".format(entity.name))
        print(u"Entity type: {}".format(language_v1.Entity.Type(entity.type_).name))
        print(u"Salience sore: {}".format(entity.salience))
        for metadata_name, metadata_value in entity.metadata.items():
            print(u"{}: {}".format(metadata_name, metadata_value))

        for mention in entity.mentions:
            print(u"Mention text: {}".format(mention.text.content))
            print(
                u"Mention type: {}".format(language_v1.EntityMention.Type(mention.type_).name)
            )

    print(u"Language of the text: {}".format(response.language))

def main():
    text = """ロシアが全土侵攻を始めたウクライナ。現地ではロシアの攻撃のものとみられる爆発音が相次ぎ、住民は恐怖で心を震わせた。"""
    analyze_entities(text)

if __name__ == "__main__":
    main()
c

gcp-nlp-api-jp-センチメント分析.py

from google.cloud import language_v1

def sample_analyze_entity_sentiment(text_content):
    """
    Analyzing Entity Sentiment in a String
    Args:
      text_content The text content to analyze
    """

    client = language_v1.LanguageServiceClient()

    type_ = language_v1.Document.Type.PLAIN_TEXT

    language = "ja"
    document = {"content": text_content, "type_": type_, "language": language}

    encoding_type = language_v1.EncodingType.UTF8

    response = client.analyze_sentiment(request = {'document': document, 'encoding_type': encoding_type})
    for sentence in response.sentences:
        print(u"Sentence text: {}".format(sentence.text.content))
        print(u"Sentence sentiment score: {}".format(sentence.sentiment.score))
        print(u"Sentence sentiment magnitude: {}".format(sentence.sentiment.magnitude))

    print(u"Language of the text: {}".format(response.language))


def main():
    import argparse

    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--text_content", type=str, default="""可能性という言葉を無限定に使ってはいけない。
        我々という存在を規定するのは、我々がもつ可能性ではなく、我々がもつ不可能性である。
        ほんの些細な決断の違いで　私の運命は変わる。
        無数の私が生まれる。"""
    )
    args = parser.parse_args()
    sample_analyze_entity_sentiment(args.text_content)


if __name__ == "__main__":
    main()