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我正在研究 Watson NLU，我需要对问卷数据进行分析。来自不同人的大约 300 个答案。我可以在“...”格式的文本上运行它，但我很想获得一些帮助，了解如何一次运行所有 300 个。我当前的输入是在带有 ID 列的 excel 中。感谢您对此进行调查。

nlu_api_key = "MY API KEY"
nlu_url = "https://api.eu-gb.natural-language-understanding.watson.cloud.ibm.com/instances/MY INSTANCE"


import json
from ibm_watson import NaturalLanguageUnderstandingV1
from ibm_cloud_sdk_core.authenticators import IAMAuthenticator
from ibm_watson.natural_language_understanding_v1 import Features, EntitiesOptions, KeywordsOptions, CategoriesOptions,SentimentOptions
import pandas as pd


gtm_Q6 = pd.read_excel(r'C:\Users\...\INPUT FILE.xlsx', sheet_name='OUPUT1')
print(gtm_Q6)


authenticator = IAMAuthenticator(nlu_api_key)
natural_language_understanding = NaturalLanguageUnderstandingV1(
    version='2020-08-01',
    authenticator=authenticator)

natural_language_understanding.set_service_url(nlu_url)

response = natural_language_understanding.analyze(
    text='Where is the firetruck with the flaming paint the tigers on top?',
    features=Features(
        entities=EntitiesOptions(emotion=True, sentiment=True, limit=5),
        keywords=KeywordsOptions(emotion=True, sentiment=True,limit=5),
        categories=CategoriesOptions(limit=3),
        sentiment=SentimentOptions(targets=['investments']) #sentiment=SentimentOptions(targets=['stocks'])
        )).get_result()

print(json.dumps(response, indent=2))
RESP_ID 回答
Q6_109.000000 团队建设
Q6_110.000000 技术和服务之间的支持和协调
Q6_111.000000 技能建设
Q6_113.000000 快速找到正确的资源
Q6_114.000000 有关当前更改的实用性的信息
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1 回答 1

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所以解决方案已经在 Python 中提供了 py TJ Crowder：这是他原始答案的链接：How to get rid of "\n" and "''" in my json file

json_arr = []
for text in gtm_Q6_df['ANSWER']:
    response = natural_language_understanding.analyze(
        text=text,language = 'en',
            features=Features(
            entities=EntitiesOptions(sentiment=True,emotion=False,limit=3),
            categories=CategoriesOptions(limit=3),
            #sentiment=SentimentOptions(EntitiesOptions)
            )).get_result()
    
    #text = ("{ Answer: " + text + "}")
    json_arr.append(text)
    json_arr.append (response) # <==== change is here
    with open(r'C:\Users\...\Documents\Python NLP\WATSON NLU\OUTPUT JSON\data.json','w') as outline: #with open('data.json','w') as outline: ## data.json is the output file created ## the file can be renamed
        json.dump(json_arr,outline, indent = 2) # indent = 2 creates the pretty json!!
    
print("break")
print(json_arr)

于 2021-02-18T14:41:15.810 回答