Use Python language to connect to Baidu's natural language processing interface to make the program intelligent

Use Python language to connect to Baidu’s natural language processing interface to make the program intelligent

With the rapid development of artificial intelligence, natural language processing has become a hot topic for research and development One of the hot spots. Baidu Natural Language Processing Interface is a powerful tool that can help us implement text sentiment analysis, lexical analysis, named entity recognition and other functions. This article will introduce how to use Python language to connect to Baidu's natural language processing interface, and demonstrate its intelligent capabilities through code examples.

First, we need to register and create a Baidu developer account, and then create a natural language processing application. During the process of creating an application, we can obtain the application's API Key and Secret Key, which will be used in subsequent code.

Next, we need to install Python’s request library requests and json parsing library json.

pip install requests
pip install json

Next, we start writing code. First, we need to import the requests and json libraries.

import requests
import json

Then, we define a function to obtain the access token of Baidu natural language processing interface. This token needs to be obtained through API Key and Secret Key, and the obtained token will be used in subsequent interface access.

def get_access_token(api_key, secret_key):
    url = 'https://aip.baidubce.com/oauth/2.0/token'
    headers = {'Content-Type': 'application/json; charset=UTF-8'}
    params = {'grant_type': 'client_credentials', 'client_id': api_key, 'client_secret': secret_key}
    response = requests.post(url, headers=headers, params=params)
    result = json.loads(response.text)
    if 'access_token' in result:
        return result['access_token']
    else:
        return None

Next, we define a function to call the text sentiment analysis function of Baidu’s natural language processing interface. This function receives the text content as a parameter and returns the sentiment analysis results.

def text_sentiment_analysis(access_token, text):
    url = 'https://aip.baidubce.com/rpc/2.0/nlp/v1/sentiment_classify?access_token=' + access_token
    headers = {'Content-Type': 'application/json'}
    data = {'text': text}
    response = requests.post(url, headers=headers, data=json.dumps(data))
    result = json.loads(response.text)
    if 'items' in result:
        sentiment = result['items'][0]['sentiment']
        confidence = result['items'][0]['confidence']
        return sentiment, confidence
    else:
        return None, None

Finally, we write the main program, call the above functions, and display the call results.

if __name__ == '__main__':
    api_key = 'your_api_key'
    secret_key = 'your_secret_key'
    access_token = get_access_token(api_key, secret_key)
    
    if access_token:
        text = input('请输入需要进行情感分析的文本：')
        sentiment, confidence = text_sentiment_analysis(access_token, text)
        
        if sentiment and confidence:
            print('文本情感分析结果：')
            print('情感：', sentiment)
            print('置信度：', confidence)
        else:
            print('调用百度自然语言处理接口失败！')
    else:
        print('获取百度自然语言处理接口的访问令牌失败！')

Through the above code example, we have implemented the call to Baidu's natural language processing interface and implemented the function of text sentiment analysis. We can call other interfaces according to our own needs to implement functions such as lexical analysis and named entity recognition.

To summarize, this article introduces how to use Python language to connect to Baidu’s natural language processing interface to achieve intelligent text analysis functions. This provides us with convenience and flexibility to implement more natural language processing tasks. I hope that through the introduction of this article, readers can flexibly use Baidu's natural language processing interface in their own projects to achieve more intelligent functions.

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