百度AipNlp和snownlp计算消费者评论文本的情感分

通过爬虫获取到电商评论文本，计算各个文本的情感分

import pandas as pd
io = r'C:\Users\beauty device comment.xlsx'
data2 = pd.read_excel(io,sheet_name="Sheet1",usecols="A:C")
data2.head(3)

第一种方法：使用snownlp计算

from snownlp import SnowNLP
sentiment_result = [] 
for sentiobj in data2["评论文本"]:
    any_obj = SnowNLP(sentiobj)
    sentiment_result.append(any_obj.sentiments)    
data2["snownlp情感分"]=sentiment_result

第二种方法：使用百度Aipnlp

from aip import AipNlp  
APP_ID = ''  
API_KEY = ''  
SECRET_KEY = ''  
client = AipNlp(APP_ID, API_KEY, SECRET_KEY)  
 
#百度情感评级
#sentiment情感极性分类结果, 0:负向，1:中性，2:正向
sentiment_baidu_result = [] 
for sentiobj2 in data2["评论文本"]:
    any_obj2 = client.sentimentClassify(sentiobj2['items'][0]['sentiment'])
    sentiment_baidu_result.append(any_obj2)
data2["百度情感评级"]=sentiment_baidu_result

#百度正向分数
import time
sentiment_baidu_positive = [] 
for sentiobj2 in data2["评论文本"]:
    any_obj2 = client.sentimentClassify(sentiobj2)
    sentiment_baidu_positive.append(any_obj2['items'][0]['positive_prob'])
data2["baidu_positive"]=sentiment_baidu_positive
 
#百度负向分数
sentiment_baidu_negative = [] 
for sentiobj2 in data2["评论文本"]:
    any_obj2 = client.sentimentClassify(sentiobj2['items'][0]['negative_prob'])
    sentiment_baidu_negative.append(any_obj2)
data2["baidu_negative"]=sentiment_baidu_negative