使用KNN算法进行BBC新闻文本分类

本文将使用KNN算法对BBC新闻数据集进行文本分类。数据集包含BBC新闻训练集 (BBC News Train.csv) 和测试集 (BBC News Test.csv)。训练集包含文本内容和对应的类别标签，测试集仅包含文本内容。

1. 数据准备

训练数据集包含两列：

ArticleId: 文章ID
Text: 文章文本内容
Category: 文章类别

测试数据集包含两列：

ArticleId: 文章ID
Text: 文章文本内容

例如，训练数据集中的文本内容实例如下：

ArticleId	Text	Category
1833	worldcom ex-boss launches defence lawyers defending former worldcom chief bernie ebbers against a battery of fraud charges have called a company whistleblower as their first witness.  'cynthia cooper'  worldcom s ex-head of internal accounting  alerted directors to irregular accounting practices at the us telecoms giant in 2002. her warnings led to the collapse of the firm following the discovery of an $11bn (拢5.7bn) accounting fraud. mr ebbers has pleaded not guilty to charges of fraud and conspiracy.  prosecution lawyers have argued that mr ebbers orchestrated a series of accounting tricks at worldcom  ordering employees to hide expenses and inflate revenues to meet wall street earnings estimates. but ms cooper  who now runs her own consulting business  told a jury in new york on wednesday that external auditors arthur andersen had approved worldcom s accounting in early 2001 and 2002. she said andersen had given a  'green light'  to the procedures and practices used by worldcom. mr ebber s lawyers have said he was unaware of the fraud  arguing that auditors did not alert him to any problems.  ms cooper also said that during shareholder meetings mr ebbers often passed over technical questions to the company s finance chief  giving only  'brief'  answers himself. the prosecution s star witness  former worldcom financial chief scott sullivan  has said that mr ebbers ordered accounting adjustments at the firm  telling him to  'hit our books' . however  ms cooper said mr sullivan had not mentioned  'anything uncomfortable'  about worldcom s accounting during a 2001 audit committee meeting. mr ebbers could face a jail sentence of 85 years if convicted of all the charges he is facing. worldcom emerged from bankruptcy protection in 2004  and is now known as mci. last week  mci agreed to a buyout by verizon communications in a deal valued at $6.75bn.	business

2. 特征提取

使用CountVectorizer将文本内容转换成特征向量。

3. 模型训练

使用KNeighborsClassifier训练KNN分类器。

4. 分类预测

使用训练好的分类器对测试集进行分类预测。

5. 结果保存

将分类结果保存到新的CSV文件 (BBC News Test Result.csv)。

代码示例

import pandas as pd
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score

# 读取训练数据集
train_data = pd.read_csv('BBC News Train.csv')

# 构建文本特征向量
vectorizer = CountVectorizer()
train_features = vectorizer.fit_transform(train_data['Text'])

# 训练KNN分类器
k = 5  # 设置K值
knn_classifier = KNeighborsClassifier(n_neighbors=k)
knn_classifier.fit(train_features, train_data['Category'])

# 读取测试数据集
test_data = pd.read_csv('BBC News Test.csv')

# 构建测试文本特征向量
test_features = vectorizer.transform(test_data['Text'])

# 进行文本分类
predicted_categories = knn_classifier.predict(test_features)

# 输出分类结果
test_data['Predicted_Category'] = predicted_categories
test_data.to_csv('BBC News Test Result.csv', index=False)

# 输出分类准确率
train_predicted = knn_classifier.predict(train_features)
train_accuracy = accuracy_score(train_predicted, train_data['Category'])
print('Train Accuracy:', train_accuracy)

总结

本文介绍了使用KNN算法对BBC新闻数据集进行文本分类的步骤。该方法简单易懂，易于实现。通过调整K值和特征提取方法，可以进一步提高分类准确率。