采用KNN算法或者SVM算法实现文本分类。python代码 编写

以下是使用KNN算法和SVM算法实现文本分类的Python代码示例：

KNN算法实现文本分类：

from sklearn.neighbors import KNeighborsClassifier
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

# 读取数据
with open('data.txt', 'r', encoding='utf-8') as f:
    data = f.readlines()

# 分离文本和标签
texts = [line.strip().split('\t')[0] for line in data]
labels = [line.strip().split('\t')[1] for line in data]

# 将文本转换为TF-IDF特征向量
vectorizer = TfidfVectorizer()
features = vectorizer.fit_transform(texts)

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.2, random_state=42)

# 训练KNN分类器
knn = KNeighborsClassifier(n_neighbors=5)
knn.fit(X_train, y_train)

# 预测测试集并计算准确率
y_pred = knn.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print('Accuracy:', accuracy)

SVM算法实现文本分类：

from sklearn.svm import SVC
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

# 读取数据
with open('data.txt', 'r', encoding='utf-8') as f:
    data = f.readlines()

# 分离文本和标签
texts = [line.strip().split('\t')[0] for line in data]
labels = [line.strip().split('\t')[1] for line in data]

# 将文本转换为TF-IDF特征向量
vectorizer = TfidfVectorizer()
features = vectorizer.fit_transform(texts)

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.2, random_state=42)

# 训练SVM分类器
svm = SVC(kernel='linear')
svm.fit(X_train, y_train)

# 预测测试集并计算准确率
y_pred = svm.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print('Accuracy:', accuracy)

需要注意的是，以上代码仅用于示例，实际应用中需要根据数据集的特点进行调参等优化