How to use C++ for efficient text mining and text analysis?

How to use C for efficient text mining and text analysis?

Overview:
Text mining and text analysis are important tasks in the fields of modern data analysis and machine learning. In this article, we will introduce how to use C language for efficient text mining and text analysis. We will focus on techniques in text preprocessing, feature extraction, and text classification, accompanied by code examples.

Text preprocessing:
Before text mining and text analysis, the original text usually needs to be preprocessed. Preprocessing includes removing punctuation, stop words, and special characters, converting to lowercase letters, and stemming. The following is a sample code using C for text preprocessing:

#include <iostream>
#include <string>
#include <algorithm>
#include <cctype>

std::string preprocessText(const std::string& text) {
    std::string processedText = text;

    // 去掉标点符号和特殊字符
    processedText.erase(std::remove_if(processedText.begin(), processedText.end(), [](char c) {
        return !std::isalnum(c) && !std::isspace(c);
    }), processedText.end());

    // 转换为小写
    std::transform(processedText.begin(), processedText.end(), processedText.begin(), [](unsigned char c) {
        return std::tolower(c);
    });

    // 进行词干化等其他操作

    return processedText;
}

int main() {
    std::string text = "Hello, World! This is a sample text.";
    std::string processedText = preprocessText(text);

    std::cout << processedText << std::endl;

    return 0;
}

Feature extraction:
When performing text analysis tasks, the text needs to be converted into a numerical feature vector so that the machine learning algorithm can process it. Commonly used feature extraction methods include bag-of-words models and TF-IDF. The following is an example code for bag-of-words model and TF-IDF feature extraction using C:

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

std::vector<std::string> extractWords(const std::string& text) {
    std::vector<std::string> words;

    // 通过空格分割字符串
    std::stringstream ss(text);
    std::string word;
    while (ss >> word) {
        words.push_back(word);
    }

    return words;
}

std::map<std::string, int> createWordCount(const std::vector<std::string>& words) {
    std::map<std::string, int> wordCount;

    for (const std::string& word : words) {
        wordCount[word]++;
    }

    return wordCount;
}

std::map<std::string, double> calculateTFIDF(const std::vector<std::map<std::string, int>>& documentWordCounts, const std::map<std::string, int>& wordCount) {
    std::map<std::string, double> tfidf;
    int numDocuments = documentWordCounts.size();

    for (const auto& wordEntry : wordCount) {
        const std::string& word = wordEntry.first;
        int wordDocumentCount = 0;

        // 统计包含该词的文档数
        for (const auto& documentWordCount : documentWordCounts) {
            if (documentWordCount.count(word) > 0) {
                wordDocumentCount++;
            }
        }

        // 计算TF-IDF值
        double tf = static_cast<double>(wordEntry.second) / wordCount.size();
        double idf = std::log(static_cast<double>(numDocuments) / (wordDocumentCount + 1));
        double tfidfValue = tf * idf;

        tfidf[word] = tfidfValue;
    }

    return tfidf;
}

int main() {
    std::string text1 = "Hello, World! This is a sample text.";
    std::string text2 = "Another sample text.";

    std::vector<std::string> words1 = extractWords(text1);
    std::vector<std::string> words2 = extractWords(text2);

    std::map<std::string, int> wordCount1 = createWordCount(words1);
    std::map<std::string, int> wordCount2 = createWordCount(words2);

    std::vector<std::map<std::string, int>> documentWordCounts = {wordCount1, wordCount2};

    std::map<std::string, double> tfidf1 = calculateTFIDF(documentWordCounts, wordCount1);
    std::map<std::string, double> tfidf2 = calculateTFIDF(documentWordCounts, wordCount2);

    // 打印TF-IDF特征向量
    for (const auto& tfidfEntry : tfidf1) {
        std::cout << tfidfEntry.first << ": " << tfidfEntry.second << std::endl;
    }

    return 0;
}

Text Classification:
Text classification is a common text mining task that divides text into different category. Commonly used text classification algorithms include Naive Bayes classifier and Support Vector Machine (SVM). The following is a sample code using C for text classification:

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

std::map<std::string, double> trainNaiveBayes(const std::vector<std::map<std::string, int>>& documentWordCounts, const std::vector<int>& labels) {
    std::map<std::string, double> classPriors;
    std::map<std::string, std::map<std::string, double>> featureProbabilities;

    int numDocuments = documentWordCounts.size();
    int numFeatures = documentWordCounts[0].size();

    std::vector<int> classCounts(numFeatures, 0);

    // 统计每个类别的先验概率和特征的条件概率
    for (int i = 0; i < numDocuments; i++) {
        std::string label = std::to_string(labels[i]);

        classCounts[labels[i]]++;

        for (const auto& wordCount : documentWordCounts[i]) {
            const std::string& word = wordCount.first;

            featureProbabilities[label][word] += wordCount.second;
        }
    }

    // 计算每个类别的先验概率
    for (int i = 0; i < numFeatures; i++) {
        double classPrior = static_cast<double>(classCounts[i]) / numDocuments;
        classPriors[std::to_string(i)] = classPrior;
    }

    // 计算每个特征的条件概率
    for (auto& classEntry : featureProbabilities) {
        std::string label = classEntry.first;
        std::map<std::string, double>& wordProbabilities = classEntry.second;

        double totalWords = 0.0;
        for (auto& wordEntry : wordProbabilities) {
            totalWords += wordEntry.second;
        }

        for (auto& wordEntry : wordProbabilities) {
            std::string& word = wordEntry.first;
            double& wordCount = wordEntry.second;

            wordCount = (wordCount + 1) / (totalWords + numFeatures);  // 拉普拉斯平滑
        }
    }

    return classPriors;
}

int predictNaiveBayes(const std::string& text, const std::map<std::string, double>& classPriors, const std::map<std::string, std::map<std::string, double>>& featureProbabilities) {
    std::vector<std::string> words = extractWords(text);
    std::map<std::string, int> wordCount = createWordCount(words);

    std::map<std::string, double> logProbabilities;

    // 计算每个类别的对数概率
    for (const auto& classEntry : classPriors) {
        std::string label = classEntry.first;
        double classPrior = classEntry.second;
        double logProbability = std::log(classPrior);

        for (const auto& wordEntry : wordCount) {
            const std::string& word = wordEntry.first;
            int wordCount = wordEntry.second;

            if (featureProbabilities.count(label) > 0 && featureProbabilities.at(label).count(word) > 0) {
                const std::map<std::string, double>& wordProbabilities = featureProbabilities.at(label);
                logProbability += std::log(wordProbabilities.at(word)) * wordCount;
            }
        }

        logProbabilities[label] = logProbability;
    }

    // 返回概率最大的类别作为预测结果
    int predictedLabel = 0;
    double maxLogProbability = -std::numeric_limits<double>::infinity();

    for (const auto& logProbabilityEntry : logProbabilities) {
        std::string label = logProbabilityEntry.first;
        double logProbability = logProbabilityEntry.second;

        if (logProbability > maxLogProbability) {
            maxLogProbability = logProbability;
            predictedLabel = std::stoi(label);
        }
    }

    return predictedLabel;
}

int main() {
    std::vector<std::string> documents = {
        "This is a positive document.",
        "This is a negative document."
    };

    std::vector<int> labels = {
        1, 0
    };

    std::vector<std::map<std::string, int>> documentWordCounts;
    for (const std::string& document : documents) {
        std::vector<std::string> words = extractWords(document);
        std::map<std::string, int> wordCount = createWordCount(words);
        documentWordCounts.push_back(wordCount);
    }

    std::map<std::string, double> classPriors = trainNaiveBayes(documentWordCounts, labels);
    int predictedLabel = predictNaiveBayes("This is a positive test document.", classPriors, featureProbabilities);

    std::cout << "Predicted Label: " << predictedLabel << std::endl;

    return 0;
}

Summary:
This article introduces how to use C for efficient text mining and text analysis, including text preprocessing, feature extraction and text classification. We show how to implement these functions through code examples, hoping to help you in practical applications. Through these technologies and tools, you can process and analyze large amounts of text data more efficiently.

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