One said one, I don't have a girlfriend, emm, the old title party, hey hey.

 

1. Introduction to text emotion analysis

Text emotion analysis: also known as opinion mining, tendency analysis, etc. In short, it is the process of analyzing, processing, summarizing and reasoning the subjective text with emotional color. The Internet (such as blogs and forums and social service networks such as public comments) has produced a large number of valuable comment information involving users, such as people, events, products and so on. These comments express people's various emotional colors and emotional tendencies, such as joy, anger, sadness, joy, criticism, praise and so on. Based on this, potential users can browse these subjective comments to understand the public opinion's views on an event or product.

2. registration / login Baidu Intelligent Cloud

Baidu account should be able to log in.

3. Language processing application technology

After login, enter "language processing application technology".

 https://console.bce.baidu.com/ai/#/ai/speech/overview/index

Enter the "management center" interface. See the figure below.

 4. Create an application and get a KEY

Write relevant information (relying solely on xia # bian). This example is text emotion analysis. Be sure to check "emotion tendency analysis". Just choose all of them. They don't want money anyway.

 

Created:

 5. Get access_token

Here you need a piece of code to get access_token.

# encoding:utf-8
#Blank template


import requests

# client_id is the API Key and client obtained on the official website_ Secret is the Secret Key obtained on the official website
host = 'https://aip. baidubce. com/oauth/2.0/token? grant_ type=client_ credentials&client_ Id = [your API Key, no square brackets required] & Client_ Secret = [your Secret Key, no square brackets required] '
response = requests.get(host)
if response:
    print(response.json())

# encoding:utf-8
import requests


#Here is my client_id，client_secret, please replace it with your own

# client_id is the API Key and client obtained on the official website_ Secret is the Secret Key obtained on the official website
host = 'https://aip.baidubce.com/oauth/2.0/token?grant_type=client_credentials&client_id=NtmIpPMmMu2nOfBlhLwwDSoT&client_secret=UnOPGxpqS7aCpPKNND1inEvXvaTFOz1i'
response = requests.get(host)
if response:
    print(response.json())

The above code is mine. Just replace it directly.

Be sure to note that the string after host is integrated without spaces. I use vscode. There is a line below it.

After running, it is like this. Although there are many contents, they are not very important, mainly "access_token":

 6. Text emotion analysis code

import re
import requests
import json

# Divide text into different segments according to lenth length
def cut_text(text, lenth):
    textArr = re.findall('.{' + str(lenth) + '}', text)
    textArr.append(text[(len(textArr) * lenth):])
    return textArr  # Returns a multi segment value

def get_emotion( data):  # Emotional analysis
    # Define the token value and URL value of Baidu API emotion analysis
    token = '24.a8a388b8d09b25d18e181d5a7dcc68c1.2592000.1629196679.282335-24567257'   #Remember to replace it with your access_token
    url = 'https://aip.baidubce.com/rpc/2.0/nlp/v1/sentiment_classify?charset=UTF-8&access_token={}'.format(token)

    # The upper limit of Baidu emotion analysis API is 2048 bytes. Therefore, if the number of bytes of the article is less than 2048, it will be called directly
    if (len(data.encode()) < 2048):
        new_each = {
            'text': data  # Save text data in variable NEW_ In each, the data type of data is string
        }
        new_each = json.dumps(new_each)
        res = requests.post(url, data=new_each)  # Using URL to request Baidu emotion analysis API
        # print("content: ", res.content)
        res_text = res.text  # Save the analysis results in string format
        result = res_text.find('items')  # Check whether there is item in the result
        positive = 1
        if (result != -1):  # If the result is not equal to - 1, the item items exists
            json_data = json.loads(res.text)
            negative = (json_data['items'][0]['negative_prob'])  # Get negative index value
            positive = (json_data['items'][0]['positive_prob'])  # Get positive index value
            print("The positive element of this sentence:",positive)
            print("The negative element of this sentence:",negative)
            # print(positive)
            if (positive > negative):  # Returns 2 if the positive is greater than the negative
                return 2
            elif (positive == negative):  # Returns 1 if negative equals positive
                return 1
            else:
                return 0  # Otherwise, return 0
        else:
            return 1
    else:
        data = cut_text(data, 1500)  # If the article byte length is greater than 1500, it will be segmented
        # print(data)
        sum_positive = 0.0  # Define the sum of positive index values
        sum_negative = 0.0  # Define the sum of negative index values
        for each in data:  # Traverse each paragraph of text
            # print(each)
            new_each = {
                'text': each  # Save text data in variable NEW_ In each
            }
            new_each = json.dumps(new_each)
            res = requests.post(url, data=new_each)  # Using URL to request Baidu emotion analysis API
            # print("content: ", res.content)
            res_text = res.text  # Save the analysis results in string format
            result = res_text.find('items')  # Check whether there is item in the result
            if (result != -1):
                json_data = json.loads(res.text)  # If the result is not equal to - 1, the item items exists
                positive = (json_data['items'][0]['positive_prob'])  # Get positive index value
                negative = (json_data['items'][0]['negative_prob'])  # Get negative index value
                sum_positive = sum_positive + positive  # Positive index sum
                sum_negative = sum_negative + negative  # Negative index sum
                # print(positive)
        print(sum_positive)
        print(sum_negative)
        if (sum_positive > sum_negative):  # Returns 2 if the positive is greater than the negative
            return 2
        elif (sum_positive == sum_negative):  # Returns 1 if negative equals positive
            return 1
        else:
            return 0  # Otherwise, return 0

def main():
    txt1 = "Is this man too bad"
    txt2 = "Be happy every day"
    print("txt1 Test results:",get_emotion(txt1))
    print("txt2 Test results:",get_emotion(txt2))

if __name__  == "__main__":
    main()

Operation results:

The text here is just a simple test. It can also be obtained by file or voice to text.

 7. Sprinkle flowers at the end

I believe you must have learned to waste, so you can't get my girlfriend!