Baidu AI interface and Golang: implement sentiment analysis and make applications smarter

Baidu AI interface and Golang: implement emotional analysis and make applications smarter

Introduction:

With the development of artificial intelligence technology, emotional analysis Plays an important role in many application scenarios. By analyzing users' emotional tendencies, applications can better understand users' needs and take appropriate measures to provide better services. Baidu AI open platform provides a wealth of API interfaces, including sentiment analysis interfaces. This article will show how to use the combination of Golang and Baidu AI interface to implement sentiment analysis functions.

1. Preparation

First, we need to register an account on Baidu AI Open Platform and create an application. During the process of creating the application, we can obtain the API Key and Secret Key, which will be used in our code.

2. Install the Go language development environment

Before we start writing code, we need to install the Golang development environment. You can download the installation package for the corresponding system from the official website (https://golang.org/dl/) and install it according to the prompts.

3. Import Golang’s HTTP and JSON libraries

We will use Golang’s HTTP library to send HTTP requests, and use the JSON library to parse the results returned by the Baidu AI interface. You can use the following command to import these two libraries:

import (
    "fmt"
    "net/http"
    "encoding/json"
)

4. Encapsulate sentiment analysis function

Next, we need to encapsulate a function to call Baidu AI's sentiment analysis interface. Here we will pass in a text parameter and return the analysis results of emotional tendency. The code is as follows:

func SentimentAnalysis(text string) (float64, error) {
    url := "https://aip.baidubce.com/rpc/2.0/nlp/v1/sentiment_classify"
    headers := map[string]string{
        "Content-Type": "application/json",
    }
    params := map[string]interface{}{
        "text": text,
    }
    auth := map[string]string{
        "APIKey":    "your_API_key",
        "SecretKey": "your_Secret_key",
    }
    jsonParams, err := json.Marshal(params)
    if err != nil {
        return 0, err
    }
    req, err := http.NewRequest("POST", url, bytes.NewBuffer(jsonParams))
    if err != nil {
        return 0, err
    }
    req.Header = headers
    q := req.URL.Query()
    q.Add("access_token", getAccessToken(auth))
    req.URL.RawQuery = q.Encode()
    client := &http.Client{}
    resp, err := client.Do(req)
    if err != nil {
        return 0, err
    }
    defer resp.Body.Close()
    var result map[string]interface{}
    err = json.NewDecoder(resp.Body).Decode(&result)
    if err != nil {
        return 0, err
    }
    sentiment := result["items"].([]interface{})[0].(map[string]interface{})["sentiment"].(map[string]interface{})["positive_prob"].(float64)
    return sentiment, nil
}

In this code, we construct an HTTP POST request according to the requirements of Baidu AI interface and pass the text parameters to the interface. We also need to obtain the access token through API Key and Secret Key for authentication.

5. Calling the sentiment analysis function

Now, we can call the encapsulated sentiment analysis function and pass a piece of text to it as a parameter. The code is as follows:

func main() {
    text := "百度AI接口与Golang结合的情感分析示例"
    sentiment, err := SentimentAnalysis(text)
    if err != nil {
        fmt.Println("情感分析失败:", err)
        return
    }
    fmt.Println("情感倾向:", sentiment)
}

Running the above code, we will get the emotional tendency result of a piece of text, which is a floating point number between 0 and 1, indicating the positive degree of emotion.

Conclusion:

Through the combination of Baidu AI interface and Golang, we can easily implement the sentiment analysis function. This brings smarter capabilities to our applications that help us better understand and serve our users' needs. I hope this article is helpful to everyone, and interested readers can try to apply this feature in their own projects.

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