一、步骤

1、将文本数据转换为特征向量 ： tf-idf

2、使用这些特征向量训练SVM模型

二、代码

from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score, classification_reportdef preprocess_data(data):texts, labels = zip(*data)vectorizer = TfidfVectorizer()X = vectorizer.fit_transform(texts).todense()return X, labels, vectorizerdef print_sorted_feature_weights(X, vectorizer):feature_name = vectorizer.get_feature_names_out()for i, doc in enumerate(X):nonzero_idx = doc.nonzero()[1]dic = {idx: doc[0, idx] for idx in nonzero_idx}sorted_dic = dict(sorted(dic.items(), key=lambda x: x[1], reverse=True))data_ = {feature_name[k]: v for k, v in sorted_dic.items()}print(data_)def train_and_evaluate_model(X_train, X_test, y_train, y_test):svm_classifier = SVC(kernel='linear', random_state=42)svm_classifier.fit(X_train, y_train)y_pred = svm_classifier.predict(X_test)return y_test, y_preddef main():# 示例数据集data = [("I love this product!", 1),("This is terrible.", 0),("The movie was fantastic.", 1),("I dislike this feature.", 0),("Amazing experience!", 1),("Not recommended.", 0)]# 数据预处理X, labels, vectorizer = preprocess_data(data)# 打印排序后的特征权重print_sorted_feature_weights(X, vectorizer)# 将数据集拆分为训练集和测试集X_train, X_test, y_train, y_test = train_test_split(X, labels, test_size=0.2, random_state=42)# 训练和评估模型y_true, y_pred = train_and_evaluate_model(X_train, X_test, y_train, y_test)# 测试集是哪些print_sorted_feature_weights(X_test,vectorizer)# 评估模型性能accuracy = accuracy_score(y_true, y_pred)report = classification_report(y_true, y_pred)# 打印模型性能指标print(f"Accuracy: {accuracy}")print("Classification Report:\n", report)if __name__ == "__main__":main()

三、结果

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