How to do emotion synthesis and emotion generation in C++?

How to perform emotion synthesis and emotion generation in C?

Abstract: Emotion synthesis and emotion generation are one of the important application areas of artificial intelligence technology. This article will introduce how to perform emotion synthesis and emotion generation in the C programming environment, and provide corresponding code examples to help readers better understand and apply these technologies.

1. Introduction
   Emotion synthesis and emotion generation are research hotspots in artificial intelligence technology, which are mainly used to simulate human emotional expression and emotion generation processes. Through machine learning and natural language processing techniques, we can train models to predict emotions and generate corresponding emotional expressions. In this article, we will introduce how to implement emotion synthesis and emotion generation through the C programming language.
2. Emotional synthesis
   Emotional synthesis refers to converting text or speech into output with corresponding emotions. A common approach is to use a sentiment dictionary to match sentiment words based on input text and evaluate sentiment scores. To perform emotion synthesis in C, you can use open source libraries such as NLTK (Natural Language Toolkit) to process emotion dictionaries.

The following is a simple C code example that implements the emotion synthesis function based on the emotion dictionary:

#include 
#include 

// 情感词典
std::unordered_map sentimentDict = {
    { "happy", 3 },
    { "sad", -2 },
    { "angry", -3 },
    // 其他情感词汇
};

// 情感合成函数
int sentimentSynthesis(const std::string& text) {
    int score = 0;
    
    // 按单词拆分文本
    std::string word;
    std::stringstream ss(text);
    while (ss >> word) {
        if (sentimentDict.find(word) != sentimentDict.end()) {
            score += sentimentDict[word];
        }
    }
    
    return score;
}

int main() {
    std::string text = "I feel happy and excited.";
    int score = sentimentSynthesis(text);
    
    std::cout << "Sentiment score: " << score << std::endl;
    
    return 0;
}

The above code performs emotion synthesis by reading the emotion dictionary, and converts the emotions in the text Sentiment words are matched against dictionaries and sentiment scores are calculated. The emotional dictionary here is just a simple example. In actual applications, more abundant emotional vocabularies can be used according to needs.

3. Emotion generation
   Emotion generation refers to the generation of text or speech based on given emotions. For emotion generation in C, you can use generative models such as recurrent neural networks (RNN) and generative adversarial networks (GAN).

The following is a simple C code example that demonstrates how to use a recurrent neural network to generate emotion-based text:

#include 
#include 

// 循环神经网络模型
struct LSTMModel : torch::nn::Module {
    LSTMModel(int inputSize, int hiddenSize, int outputSize)
        : lstm(torch::nn::LSTMOptions(inputSize, hiddenSize).layers(1)),
          linear(hiddenSize, outputSize) {
        register_module("lstm", lstm);
        register_module("linear", linear);
    }

    torch::Tensor forward(torch::Tensor input) {
        auto lstmOut = lstm(input);
        auto output = linear(std::get<0>(lstmOut)[-1]);
        return output;
    }

    torch::nn::LSTM lstm;
    torch::nn::Linear linear;
};

int main() {
    torch::manual_seed(1);

    // 训练数据
    std::vector happySeq = { 0, 1, 2, 3 }; // 对应编码
    std::vector sadSeq = { 4, 5, 6, 3 };
    std::vector angrySeq = { 7, 8, 9, 3 };
    std::vector> sequences = { happySeq, sadSeq, angrySeq };

    // 情感编码与文本映射
    std::unordered_map sentimentDict = {
        { 0, "I" },
        { 1, "feel" },
        { 2, "happy" },
        { 3, "." },
        { 4, "I" },
        { 5, "feel" },
        { 6, "sad" },
        { 7, "I" },
        { 8, "feel" },
        { 9, "angry" }
    };

    // 构建训练集
    std::vector inputs, targets;
    for (const auto& seq : sequences) {
        torch::Tensor input = torch::zeros({ seq.size()-1, 1, 1 });
        torch::Tensor target = torch::zeros({ seq.size()-1 });
        for (size_t i = 0; i < seq.size() - 1; ++i) {
            input[i][0][0] = seq[i];
            target[i] = seq[i + 1];
        }
        inputs.push_back(input);
        targets.push_back(target);
    }

    // 模型参数
    int inputSize = 1;
    int hiddenSize = 16;
    int outputSize = 10;

    // 模型
    LSTMModel model(inputSize, hiddenSize, outputSize);
    torch::optim::Adam optimizer(model.parameters(), torch::optim::AdamOptions(0.01));

    // 训练
    for (int epoch = 0; epoch < 100; ++epoch) {
        for (size_t i = 0; i < inputs.size(); ++i) {
            torch::Tensor input = inputs[i];
            torch::Tensor target = targets[i];

            optimizer.zero_grad();
            torch::Tensor output = model.forward(input);
            torch::Tensor loss = torch::nn::functional::nll_loss(torch::log_softmax(output, 1).squeeze(), target);
            loss.backward();
            optimizer.step();
        }
    }

    // 生成
    torch::Tensor input = torch::zeros({ 1, 1, 1 });
    input[0][0][0] = 0; // 输入情感：happy
    std::cout << sentimentDict[0] << " ";
    for (int i = 1; i < 5; ++i) {
        torch::Tensor output = model.forward(input);
        int pred = output.argmax().item();
        std::cout << sentimentDict[pred] << " ";
        input[0][0][0] = pred;
    }
    std::cout << std::endl;

    return 0;
}

The above code uses the LibTorch library to implement a simple Recurrent neural network model. By training a series of emotion sequences, the corresponding text sequence is generated given the emotion. During the training process, we use negative log-likelihood loss to measure the difference between the prediction results and the target, and use the Adam optimizer to update the model parameters.

4. Summary
   This article introduces how to perform emotion synthesis and emotion generation in a C programming environment. Emotion synthesis uses emotional dictionaries to perform emotional analysis on text to achieve the function of emotion synthesis; emotion generation uses generative models to generate emotion-based text sequences. We provide corresponding code examples, hoping to help readers better understand and apply the technology of emotion synthesis and emotion generation. Of course, this is just a simple example, and it can be optimized and expanded according to specific needs in actual applications.

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