How to perform sentiment analysis and emotion recognition in C++?

How to perform sentiment analysis and emotion recognition in C?

Introduction:
In today's social media and Internet era, people generate a large amount of text data, which contains rich emotional colors. Sentiment analysis and emotion recognition have become an important task, which can help us understand and analyze people's emotions and emotional states in different scenarios. This article will introduce how to implement sentiment analysis and emotion recognition in C, and attach code examples to help readers understand and apply related technologies.

1. Background and definition of sentiment analysis
Sentiment analysis, also known as emotion discrimination, emotion recognition, etc., refers to processing input such as text or speech to determine the emotional tendency expressed in it. Common sentiment analysis tasks include sentiment classification (positive, negative, neutral) and sentiment intensity analysis (positive, negative, neutral degree). For example, sentiment analysis of reviews of a product on social media can help companies understand how satisfied users are with the product and where to improve it.

2. Implementation methods of sentiment analysis and emotion recognition
In C, technologies such as machine learning and natural language processing (NLP) can be used to implement sentiment analysis and emotion recognition. Several commonly used methods will be introduced below.

1. Rule-based method
   The rule-based method is a simple and intuitive sentiment analysis method. It determines emotional tendencies by defining a series of rules or keywords and judging whether these rules or keywords appear in the text. For example, we can define some positive keywords (such as "good", "like") and negative keywords (such as "bad", "hate"), then match the text and calculate the positive keywords and negative keywords The number of occurrences of words is used to determine emotional tendencies.

The following is a simple rule-based sentiment analysis code example:

#include <iostream>
#include <string>

int main() {
    std::string text;
    std::cout << "请输入一段文本：";
    std::getline(std::cin, text);
    
    int positiveCount = 0;
    int negativeCount = 0;
    
    // 定义积极和消极的关键词
    std::string positiveWords[] = {"好", "喜欢"};
    std::string negativeWords[] = {"不好", "讨厌"};
    
    // 判断文本中的关键词出现次数
    for (auto word : positiveWords) {
        size_t pos = text.find(word);
        while (pos != std::string::npos) {
            positiveCount++;
            pos = text.find(word, pos + 1);
        }
    }
    
    for (auto word : negativeWords) {
        size_t pos = text.find(word);
        while (pos != std::string::npos) {
            negativeCount++;
            pos = text.find(word, pos + 1);
        }
    }
    
    // 根据关键词出现次数判断情感倾向
    if (positiveCount > negativeCount) {
        std::cout << "积极情感" << std::endl;
    } else if (positiveCount < negativeCount) {
        std::cout << "消极情感" << std::endl;
    } else {
        std::cout << "中性情感" << std::endl;
    }
    
    return 0;
}

After running the program, enter a piece of text, and the program will calculate the number of occurrences of positive and negative keywords in the text. , determine emotional tendencies and output results.

2. Machine learning-based method
   The machine learning-based method is a more accurate and automated sentiment analysis method. It builds an emotion classification model, learns the characteristics and rules of different emotions from a large amount of annotated data, and predicts new texts. Commonly used machine learning algorithms include Naive Bayes, Support Vector Machine, and deep learning.

The following is a sentiment analysis code example based on the Naive Bayes algorithm (using OpenCV's ml module):

#include <iostream>
#include <opencv2/opencv.hpp>

int main() {
    std::string text;
    std::cout << "请输入一段文本：";
    std::getline(std::cin, text);
    
    cv::Ptr<cv::ml::NaiveBayes> model = cv::ml::NaiveBayes::create();
    
    // 加载已经训练好的模型
    model->load("model.xml");
    
    // 提取文本特征
    cv::Mat feature(1, text.size(), CV_32FC1);
    for (int i = 0; i < text.size(); i++) {
        feature.at<float>(0, i) = text[i];
    }
    
    // 预测情感
    int result = model->predict(feature);
    
    if (result == 0) {
        std::cout << "积极情感" << std::endl;
    } else if (result == 1) {
        std::cout << "消极情感" << std::endl;
    } else {
        std::cout << "中性情感" << std::endl;
    }
    
    return 0;
}

After running the program, enter a piece of text and the program will load the The trained emotion classification model predicts based on text features and outputs emotional tendencies.

3. Summary
This article introduces how to implement sentiment analysis and emotion recognition in C, and provides two code examples based on rules and machine learning. Readers can choose appropriate methods and tools based on specific tasks and data characteristics to practice and apply sentiment analysis and emotion recognition. Sentiment analysis and emotion recognition can help us better understand and respond to people's emotional needs, and improve the quality and user experience of products and services.

References:

1. Zhang Ding, "Research on Chinese Sentiment Classification Based on Naive Bayes";
2. Tutorial: Basic Sentiment Analysis of Machine Learning, URL: https://blog.csdn.net/weixin_41190227/article/details/113689859.

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