如何使用C++进行高效的文本挖掘 文本分析
概述：
文本预处理：
#include #include #include #include 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;}
特征提取：
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#include #include #include #include #include std::vector extractWords(const std::string& text) { std::vector 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 createWordCount(const std::vector& words) { std::map wordCount; for (const std::string& word : words) { wordCount[word]++; } return wordCount;}std::map calculateTFIDF(const std::vector<std::map>& documentWordCounts, const std::map& wordCount) { std::map 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(wordEntry.second) / wordCount.size(); double idf = std::log(static_cast(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 words1 = extractWords(text1); std::vector words2 = extractWords(text2); std::map wordCount1 = createWordCount(words1); std::map wordCount2 = createWordCount(words2); std::vector<std::map> documentWordCounts = {wordCount1, wordCount2}; std::map tfidf1 = calculateTFIDF(documentWordCounts, wordCount1); std::map 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;}
文本分类：
#include #include #include #include #include std::map trainNaiveBayes(const std::vector<std::map>& documentWordCounts, const std::vector& labels) { std::map classPriors; std::map<std::string, std::map> featureProbabilities; int numDocuments = documentWordCounts.size(); int numFeatures = documentWordCounts[0].size(); std::vector 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(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& 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& classPriors, const std::map<std::string, std::map>& featureProbabilities) { std::vector words = extractWords(text); std::map wordCount = createWordCount(words); std::map 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& 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::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 documents = { "This is a positive document.", "This is a negative document." }; std::vector labels = { 1, 0 }; std::vector<std::map> documentWordCounts; for (const std::string& document : documents) { std::vector words = extractWords(document); std::map wordCount = createWordCount(words); documentWordCounts.push_back(wordCount); } std::map classPriors = trainNaiveBayes(documentWordCounts, labels); int predictedLabel = predictNaiveBayes("This is a positive test document.", classPriors, featureProbabilities); std::cout << "Predicted Label: " << predictedLabel << std::endl; return 0;}
总结：
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