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

How to perform emotion recognition and sentiment analysis in C?

Overview:
Emotion recognition and sentiment analysis are one of the important applications in the field of natural language processing. It can help us understand the emotional color in text, and plays an important role in public opinion monitoring, sentiment analysis and other scenarios. This article will introduce how to implement the basic methods of emotion recognition and emotion analysis in C, and provide corresponding code examples.

1. Data preparation
   To perform emotion recognition and sentiment analysis, you first need to prepare a data set suitable for the task. Datasets typically contain a large number of annotated text samples, each with an emotional category label (such as positive, negative, or neutral). Public data sets can be used, such as IMDb movie evaluation data, Twitter sentiment analysis data, etc. You can also collect data yourself and label it manually.
2. Text preprocessing
   Before performing sentiment analysis, the original text needs to be preprocessed. The main goal of preprocessing is to remove noise and irrelevant information, making the text more suitable for subsequent feature extraction and classification. Common preprocessing steps include: punctuation removal, stop word filtering, word stemming, etc. In C, you can use existing text processing libraries, such as Boost library and NLTK library, to complete these tasks.
3. Feature extraction
   Feature extraction is the core step of emotion recognition and emotion analysis. By converting text into feature vectors, machine learning algorithms can be helped to better understand and classify the sentiment of text. Common feature extraction methods include: bag-of-words model, TF-IDF, word vector, etc. In C, third-party libraries, such as LIBSVM library and GloVe library, can be used to implement feature extraction.

The following is a simple sample code that demonstrates how to use the bag-of-words model for feature extraction:

#include <iostream>
#include <vector>
#include <map>
#include <string>

using namespace std;

// 构建词袋模型
map<string, int> buildBagOfWords(const vector<string>& document) {
    map<string, int> wordCount;
    for (const auto& word : document) {
        wordCount[word]++;
    }
    return wordCount;
}

int main() {
    // 原始文本
    vector<string> document = {"I", "love", "this", "movie", "it", "is", "amazing"};

    // 构建词袋模型
    map<string, int> bagOfWords = buildBagOfWords(document);

    // 输出词袋模型
    for (const auto& entry : bagOfWords) {
        cout << entry.first << ": " << entry.second << endl;
    }

    return 0;
}

4. Model training and classification
   After completing feature extraction , the model can be trained using machine learning algorithms and used to classify new texts emotionally. Commonly used machine learning algorithms include naive Bayes, support vector machines, deep learning, etc. Existing machine learning libraries, such as MLlib library and TensorFlow library, can be used in C to complete model training and classification.

The following is a simple sample code that demonstrates how to use the Naive Bayes algorithm for sentiment classification:

#include <iostream>
#include <map>
#include <vector>

using namespace std;

// 训练朴素贝叶斯模型
map<string, double> trainNaiveBayesModel(const vector<vector<string>>& trainingData, const vector<string>& labels) {
    map<string, double> model;

    // 统计每个词在正面和负面样本中出现的次数
    int numPositiveWords = 0, numNegativeWords = 0;
    map<string, int> positiveWordCount, negativeWordCount;
    for (int i = 0; i < trainingData.size(); ++i) {
        const auto& document = trainingData[i];
        const auto& label = labels[i];

        for (const auto& word : document) {
            if (label == "positive") {
                positiveWordCount[word]++;
                numPositiveWords++;
            } else if (label == "negative") {
                negativeWordCount[word]++;
                numNegativeWords++;
            }
        }
    }

    // 计算每个词在正面和负面样本中的概率
    for (const auto& entry : positiveWordCount) {
        const auto& word = entry.first;
        const auto& count = entry.second;

        model[word] = (count + 1) / double(numPositiveWords + positiveWordCount.size());
    }

    for (const auto& entry : negativeWordCount) {
        const auto& word = entry.first;
        const auto& count = entry.second;

        model[word] = (count + 1) / double(numNegativeWords + negativeWordCount.size());
    }

    return model;
}

// 利用朴素贝叶斯模型进行情感分类
string classifyDocument(const vector<string>& document, const map<string, double>& model) {
    double positiveProbability = 0, negativeProbability = 0;
    for (const auto& word : document) {
        if (model.count(word) > 0) {
            positiveProbability += log(model.at(word));
            negativeProbability += log(1 - model.at(word));
        }
    }

    if (positiveProbability > negativeProbability) {
        return "positive";
    } else {
        return "negative";
    }
}

int main() {
    // 训练数据和标签
    vector<vector<string>> trainingData = {{"I", "love", "this", "movie"},
                                           {"I", "hate", "this", "movie"},
                                           {"It", "is", "amazing"},
                                           {"It", "is", "terrible"}};
    vector<string> labels = {"positive", "negative", "positive", "negative"};

    // 训练朴素贝叶斯模型
    map<string, double> model = trainNaiveBayesModel(trainingData, labels);

    // 对新的文本进行情感分类
    vector<string> document = {"I", "love", "this", "movie"};
    string sentiment = classifyDocument(document, model);

    cout << "Sentiment of the document: " << sentiment << endl;

    return 0;
}

Summary:
This article describes how to implement it in C Basic methods of emotion recognition and sentiment analysis. Through steps such as preprocessing, feature extraction, model training, and classification, we can accurately judge and classify the sentiment of text. At the same time, we also provide corresponding code examples to help readers better understand and practice emotion recognition and emotion analysis technology. Hope this article is helpful to everyone.

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