朴素贝叶斯实现文本分类代码

下面是一个基于朴素贝叶斯算法实现文本分类的示例代码：

import numpy as np

# 定义训练集和测试集
train_data = [
    ['I love this sandwich', 'pos'],
    ['This is an amazing place', 'pos'],
    ['I feel very good about these beers', 'pos'],
    ['This is my best work', 'pos'],
    ['What an awesome view', 'pos'],
    ['I do not like this restaurant', 'neg'],
    ['I am tired of this stuff', 'neg'],
    ['I can't deal with this', 'neg'],
    ['He is my sworn enemy', 'neg'],
    ['My boss is horrible', 'neg']
]

test_data = [
    'The beer was good',
    'I do not enjoy my job',
    'I ain’t feeling dandy today',
    'I feel amazing!'
]

# 分离出训练集中的文本和标签
train_texts, train_labels = zip(*train_data)

# 将文本转化为词向量
vocab = sorted(set(word for text in train_texts for word in text.split()))
word2idx = {word: i for i, word in enumerate(vocab)}
train_vectors = np.zeros((len(train_data), len(vocab)))
for i, text in enumerate(train_texts):
    for word in text.split():
        train_vectors[i, word2idx[word]] += 1

# 训练模型
pos_vectors = train_vectors[train_labels == 'pos']
neg_vectors = train_vectors[train_labels == 'neg']
pos_prior = len(pos_vectors) / len(train_labels)
neg_prior = len(neg_vectors) / len(train_labels)
pos_likelihood = (pos_vectors.sum(axis=0) + 1) / (pos_vectors.sum() + len(vocab))
neg_likelihood = (neg_vectors.sum(axis=0) + 1) / (neg_vectors.sum() + len(vocab))

# 对测试集进行分类
for text in test_data:
    vector = np.zeros(len(vocab))
    for word in text.split():
        if word in word2idx:
            vector[word2idx[word]] += 1
    pos_score = np.log(pos_prior) + np.sum(np.log(pos_likelihood) * vector)
    neg_score = np.log(neg_prior) + np.sum(np.log(neg_likelihood) * vector)
    if pos_score > neg_score:
        print(f"{text} is positive")
    else:
        print(f"{text} is negative")

其中，训练集和测试集分别包含文本和标签。将训练集中的文本转化为词向量，并计算每个词在正类和负类中出现的概率。对于测试集中的每个文本，将其转化为词向量，并利用朴素贝叶斯算法计算其属于正类和负类的概率得分。最终，将得分较高的类别作为预测结果输出。