The perfect combination of Baidu AI interface and Golang to build an intelligent text analysis system

The perfect combination of Baidu AI interface and Golang to build an intelligent text analysis system

Introduction:
With the continuous development of artificial intelligence technology, text analysis has become a lot important part of the application field. Baidu AI interface provides a series of powerful text analysis functions, such as sentiment analysis, text classification, named entity recognition, etc. Golang, as a simple and efficient programming language, has good concurrency capabilities and cross-platform features. This article will explore how to use Golang combined with Baidu AI interface to build an intelligent text analysis system, and provide corresponding sample code.

1. Installing Golang
   First, we need to install Golang. The latest version of Golang can be downloaded and installed from the official website. After the installation is complete, we can enter "go version" on the command line to confirm whether the installation is successful.
2. Use Baidu AI interface
   Baidu AI interface is a series of artificial intelligence services provided by Baidu Cloud to developers. Before using the Baidu AI interface, we need to create a Baidu Cloud account and create an application to obtain the API Key and Secret Key. For specific operation steps, please refer to Baidu Cloud official documentation.
3. Text Sentiment Analysis
   Baidu AI interface provides the function of sentiment analysis, which can perform emotional judgment on a piece of text and return an emotional polarity score. The following is a simple Golang code example:

package main

import (
    "encoding/json"
    "fmt"
    "io/ioutil"
    "net/http"
    "strings"
)

const (
    BaiduAPIKey    = "your-api-key"
    BaiduSecretKey = "your-secret-key"
)

type SentimentAnalysisResponse struct {
    Text   string `json:"text"`
    Score  int    `json:"score"`
    ErrMsg string `json:"errMsg"`
}

func main() {
    text := "这家餐厅的菜品非常好吃！"

    url := "https://aip.baidubce.com/rpc/2.0/nlp/v1/sentiment_classify"

    payload := strings.NewReader(fmt.Sprintf(`{
        "text": "%s"
    }`, text))

    client := &http.Client{}
    req, err := http.NewRequest("POST", url, payload)
    if err != nil {
        panic(err)
    }

    req.Header.Add("Content-Type", "application/json")
    req.Header.Add("charset", "UTF-8")
    req.Header.Add("Accept", "application/json")
    req.Header.Add("Authorization", fmt.Sprintf("Bearer %s", BaiduAPIKey))

    res, err := client.Do(req)
    if err != nil {
        panic(err)
    }
    defer res.Body.Close()

    body, _ := ioutil.ReadAll(res.Body)

    var response SentimentAnalysisResponse
    err = json.Unmarshal(body, &response)
    if err != nil {
        panic(err)
    }

    if response.ErrMsg != "" {
        panic(response.ErrMsg)
    }

    fmt.Printf("Input text: %s
", response.Text)
    fmt.Printf("Sentiment score: %d
", response.Score)
}

In the above code, we first define a structure SentimentAnalysisResponse, which is used to parse the JSON data returned by the Baidu AI interface. Then, we constructed a request based on the documentation of Baidu AI Interface and sent it to Baidu AI Interface. Finally, we parse the data returned by the interface and output the sentiment analysis results.

4. Text Classification
   Baidu AI interface can also perform text classification, dividing a piece of text into predefined categories. The following is a simple Golang code example:

package main

import (
    "encoding/json"
    "fmt"
    "io/ioutil"
    "net/http"
    "strings"
)

const (
    BaiduAPIKey    = "your-api-key"
    BaiduSecretKey = "your-secret-key"
)

type TextClassificationResponse struct {
    Text   string `json:"text"`
    Class  string `json:"class"`
    ErrMsg string `json:"errMsg"`
}

func main() {
    text := "苹果新推出的iPhone SE性价比很高！"

    url := "https://aip.baidubce.com/rpc/2.0/nlp/v1/topic"

    payload := strings.NewReader(fmt.Sprintf(`{
        "title": "%s"
    }`, text))

    client := &http.Client{}
    req, err := http.NewRequest("POST", url, payload)
    if err != nil {
        panic(err)
    }

    req.Header.Add("Content-Type", "application/json")
    req.Header.Add("charset", "UTF-8")
    req.Header.Add("Accept", "application/json")
    req.Header.Add("Authorization", fmt.Sprintf("Bearer %s", BaiduAPIKey))

    res, err := client.Do(req)
    if err != nil {
        panic(err)
    }
    defer res.Body.Close()

    body, _ := ioutil.ReadAll(res.Body)

    var response TextClassificationResponse
    err = json.Unmarshal(body, &response)
    if err != nil {
        panic(err)
    }

    if response.ErrMsg != "" {
        panic(response.ErrMsg)
    }

    fmt.Printf("Input text: %s
", response.Text)
    fmt.Printf("Class: %s
", response.Class)
}

In the above code, we define a structure TextClassificationResponse for parsing the JSON data returned by the Baidu AI interface. Then, we constructed a request and sent it to Baidu AI interface. Finally, we parse the data returned by the interface and output the text classification results.

Conclusion:
By using the combination of Golang and Baidu AI interface, we can quickly build an intelligent text analysis system. In this article, we introduce how to use Golang to write code to call the sentiment analysis and text classification functions of Baidu AI interface. Of course, Baidu AI interface also provides many other useful text analysis functions, which readers can adjust and expand accordingly according to their own needs. I hope this article can provide readers with some useful references in building intelligent text analysis systems.

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