数据集corpus文件夹该文件夹下包含两个文件夹pos文件夹和neg文件夹使用python语言采用KNN算法帮我做一个非常详细的文本分类停用词表采用哈工大停用词表txt

首先，我们需要导入所需的库：

import os
import re
import jieba
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import train_test_split

接下来，我们需要加载数据集并对文本进行分词和预处理。在这个例子中，我们使用中文文本，因此需要使用jieba库进行分词。我们还需要去除停用词，可以使用哈工大停用词表.txt。

stop_words = set()
with open('哈工大停用词表.txt', 'r', encoding='utf-8') as f:
    for line in f.readlines():
        stop_words.add(line.strip())

def preprocess_text(text):
    text = re.sub('[^\u4e00-\u9fa5]', '', text)  # 只保留中文
    words = jieba.cut(text)
    words = [word for word in words if word not in stop_words and len(word) > 1]
    return ' '.join(words)

corpus = []
labels = []
for folder in ['pos', 'neg']:
    files = os.listdir(os.path.join('corpus', folder))
    for file in files:
        with open(os.path.join('corpus', folder, file), 'r', encoding='utf-8') as f:
            text = f.read()
            text = preprocess_text(text)
            corpus.append(text)
            labels.append(1 if folder == 'pos' else 0)

接下来，我们将文本转换成TF-IDF特征向量。这可以使用TfidfVectorizer库来实现。

vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(corpus)
y = labels

现在我们准备好将数据集划分为训练集和测试集。我们将使用train_test_split函数进行此操作。

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

最后，我们使用KNN算法进行分类。我们可以使用sklearn.neighbors.KNeighborsClassifier库来实现。

knn = KNeighborsClassifier(n_neighbors=5)
knn.fit(X_train, y_train)
score = knn.score(X_test, y_test)
print('Accuracy: {:.2f}%'.format(score*100))

完整代码如下：

import os
import re
import jieba
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import train_test_split

stop_words = set()
with open('哈工大停用词表.txt', 'r', encoding='utf-8') as f:
    for line in f.readlines():
        stop_words.add(line.strip())

def preprocess_text(text):
    text = re.sub('[^\u4e00-\u9fa5]', '', text)  # 只保留中文
    words = jieba.cut(text)
    words = [word for word in words if word not in stop_words and len(word) > 1]
    return ' '.join(words)

corpus = []
labels = []
for folder in ['pos', 'neg']:
    files = os.listdir(os.path.join('corpus', folder))
    for file in files:
        with open(os.path.join('corpus', folder, file), 'r', encoding='utf-8') as f:
            text = f.read()
            text = preprocess_text(text)
            corpus.append(text)
            labels.append(1 if folder == 'pos' else 0)

vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(corpus)
y = labels

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

knn = KNeighborsClassifier(n_neighbors=5)
knn.fit(X_train, y_train)
score = knn.score(X_test, y_test)
print('Accuracy: {:.2f}%'.format(score*100))

这个例子中，我们使用了KNN算法进行文本分类。在实际应用中，您可以尝试其他机器学习算法和特征选择方法，以获得更好的结果