用python代码实现THUCNews数据集文本分类
时间: 2024-01-26 11:07:40 浏览: 40
好的,下面是用Python代码实现THUCNews数据集文本分类的步骤:
1. 下载并解压THUCNews数据集,可以在这里下载:http://thuctc.thunlp.org/#数据集-1
2. 安装必要的Python库,包括jieba、sklearn和numpy。可以使用以下命令进行安装:
```python
pip install jieba sklearn numpy
```
3. 加载并预处理数据。使用Python的pandas库读取数据集,并使用jieba库进行中文分词和停用词过滤。将分词后的文本转换为词向量表示。
```python
import pandas as pd
import jieba
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import train_test_split
# 读取数据集
data = pd.read_csv('thucnews.csv')
# 分词和停用词过滤
stopwords = [line.strip() for line in open('stopwords.txt', 'r', encoding='utf-8').readlines()]
data['content'] = data['content'].apply(lambda x: [word for word in jieba.cut(x) if word not in stopwords])
# 将文本转换为词向量表示
corpus = data['content'].apply(lambda x: ' '.join(x))
vectorizer = TfidfVectorizer(max_features=5000)
X = vectorizer.fit_transform(corpus).toarray()
y = data['category'].values
```
4. 划分训练集和测试集。使用sklearn库的train_test_split函数将数据集划分为训练集和测试集。
```python
# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
```
5. 训练和评估分类器。使用sklearn库的SVM分类器进行训练和评估。
```python
from sklearn.svm import SVC
from sklearn.metrics import classification_report
# 训练分类器
clf = SVC(kernel='linear')
clf.fit(X_train, y_train)
# 在测试集上评估分类器
y_pred = clf.predict(X_test)
print(classification_report(y_test, y_pred))
```
完整的代码如下所示:
```python
import pandas as pd
import jieba
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.metrics import classification_report
# 读取数据集
data = pd.read_csv('thucnews.csv')
# 分词和停用词过滤
stopwords = [line.strip() for line in open('stopwords.txt', 'r', encoding='utf-8').readlines()]
data['content'] = data['content'].apply(lambda x: [word for word in jieba.cut(x) if word not in stopwords])
# 将文本转换为词向量表示
corpus = data['content'].apply(lambda x: ' '.join(x))
vectorizer = TfidfVectorizer(max_features=5000)
X = vectorizer.fit_transform(corpus).toarray()
y = data['category'].values
# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# 训练分类器
clf = SVC(kernel='linear')
clf.fit(X_train, y_train)
# 在测试集上评估分类器
y_pred = clf.predict(X_test)
print(classification_report(y_test, y_pred))
```
注意:上面的代码中使用的是线性SVM分类器,可以根据需要选择其他分类器。此外,还需要下载停用词表文件stopwords.txt,可以从网上下载或自己构建。