Quick Start: Use Go language functions to implement simple text classification functions

Quick Start: Use Go language functions to implement simple text classification functions

Text classification is an important task in the field of natural language processing. Its goal is to assign a given piece of text to a predefined category. In this article, we will use Go language functions to implement a simple text classification function.

First, we need to clarify the specific goals of this simple text classification problem. In this example, our goal is to classify a piece of text into two categories: positive and negative. We will use a method based on keyword matching to achieve this.

Next, we need to prepare a dictionary containing positive keywords and negative keywords. These keywords can be words related to positive or negative emotions, such as "good", "like" and other words that express positive emotions, and "bad" and "hate" and other words that express negative emotions. We can store these keywords in a string slice.

Then, we can write a function to accept a piece of text as input and determine whether the text is a positive emotion or a negative emotion. Here is a sample code:

package main

import (
    "fmt"
    "strings"
)

func classifyText(text string, positiveKeywords []string, negativeKeywords []string) string {
    text = strings.ToLower(text) // 将文本转换为小写
    for _, keyword := range positiveKeywords { // 遍历正面关键词
        if strings.Contains(text, keyword) { // 如果文本包含正面关键词
            return "Positive" // 返回正面情感
        }
    }
    for _, keyword := range negativeKeywords { // 遍历负面关键词
        if strings.Contains(text, keyword) { // 如果文本包含负面关键词
            return "Negative" // 返回负面情感
        }
    }
    return "Neutral" // 如果文本既不包含正面关键词也不包含负面关键词，则返回中性情感
}

func main() {
    text := "我很喜欢这个产品" // 要分类的文本
    positiveKeywords := []string{"好", "喜欢"} // 正面关键词
    negativeKeywords := []string{"坏", "讨厌"} // 负面关键词
    result := classifyText(text, positiveKeywords, negativeKeywords)
    fmt.Println("文本分类结果：", result)
}

In the above code, we define a classifyText function that accepts three parameters: text, positive keyword slices, and negative keyword slices. The function first converts the input text to lowercase, then iterates through the positive keywords and negative keywords, and uses the strings.Contains function to determine whether the text contains the keywords. Returns "Positive" if the text contains positive keywords, "Negative" if the text contains negative keywords, and "Neutral" if the text contains neither positive nor negative keywords.

In the main function, we define a text to be classified, as well as positive keywords and negative keywords. Then we call the classifyText function and print the result.

With the above code, we can perform a simple classification of positive and negative sentiment on the given text.

Of course, this is just a simple example, and the actual text classification problem may be more complex. However, by using functions and keyword matching, we can quickly get started and implement a simple text classification function.

I hope this article will help you understand how to use Go language functions to implement text classification functions!

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