我有一些示例代碼，可以使用 Google 的自然語(yǔ)言 API 來(lái)分析實(shí)體及其情緒。對(duì)于 Pandas 數(shù)據(jù)框中的每條記錄，我想返回一個(gè)字典列表，其中每個(gè)元素都是一個(gè)實(shí)體。然而，當(dāng)我嘗試讓它在生產(chǎn)數(shù)據(jù)上工作時(shí)遇到了問(wèn)題。這是示例代碼from google.cloud import language_v1 # version 2.0.0import os os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = 'path/to/json'import pandas as pd # establish client connectionclient = language_v1.LanguageServiceClient()# helper function def custom_analyze_entity(text_content):    global client    #print("Accepted Input::" + text_content)    document = language_v1.Document(content=text_content, type_=language_v1.Document.Type.PLAIN_TEXT, language = 'en')    response = client.analyze_entity_sentiment(request = {'document': document})    # a document can have many entities    # create a list of dictionaries, every element in the list is a dictionary that represents an entity    # the dictionary is nested    l = []    #print("Entity response:" + str(response.entities))    for entity in response.entities:        #print('=' * 20)        temp_dict = {}        temp_meta_dict = {}        temp_mentions = {}        temp_dict['name'] = entity.name        temp_dict['type'] = language_v1.Entity.Type(entity.type_).name        temp_dict['salience'] = str(entity.salience)        sentiment = entity.sentiment        temp_dict['sentiment_score'] = str(sentiment.score)        temp_dict['sentiment_magnitude'] = str(sentiment.magnitude)        for metadata_name, metadata_value in entity.metadata.items():            temp_meta_dict['metadata_name'] = metadata_name            temp_meta_dict['metadata_value'] = metadata_value        temp_dict['metadata'] = temp_meta_dict        for mention in entity.mentions:            temp_mentions['mention_text'] = str(mention.text.content)            temp_mentions['mention_type'] = str(language_v1.EntityMention.Type(mention.type_).name)        temp_dict['mentions'] = temp_mentions        #print(u"Appended Entity::: {}".format(temp_dict))        l.append(temp_dict)    return l