用python实现情感词典生成生成情感词然后朴素贝叶斯再去分类的代码

好的，下面是一个简单的情感词典生成和朴素贝叶斯分类的Python代码示例：

import os
import re
from collections import Counter
import math

# 情感词典文件路径
SENTIMENT_DICT_FILE = 'sentiment_dict.txt'

# 训练集文件路径
TRAIN_DATA_FILE = 'train_data.txt'

# 测试集文件路径
TEST_DATA_FILE = 'test_data.txt'

# 情感词典生成函数
def generate_sentiment_dict(train_data_file, output_file):
    sentiment_words = Counter()
    with open(train_data_file, 'r', encoding='utf-8') as f:
        for line in f:
            line = line.strip()
            if not line:
                continue
            words, label = line.split('\t')
            if label == '1':
                sentiment_words.update(words.split(' '))
    with open(output_file, 'w', encoding='utf-8') as f:
        for word, freq in sentiment_words.items():
            f.write('{}\t{}\n'.format(word, freq))

# 朴素贝叶斯分类器
class NaiveBayesClassifier:
    def __init__(self):
        self.total_docs = 0  # 总文档数
        self.class_docs = {}  # 每个类别的文档数
        self.word_freq = {}  # 每个词在每个类别中的出现次数
        self.classes = set()  # 类别集合
        self.vocab = set()  # 词汇表

    # 训练函数
    def train(self, train_data_file):
        with open(train_data_file, 'r', encoding='utf-8') as f:
            for line in f:
                line = line.strip()
                if not line:
                    continue
                words, label = line.split('\t')
                self.total_docs += 1
                self.class_docs[label] = self.class_docs.get(label, 0) + 1
                for word in words.split(' '):
                    self.word_freq[label] = self.word_freq.get(label, Counter())
                    self.word_freq[label][word] += 1
                    self.vocab.add(word)
                self.classes.add(label)

    # 预测函数
    def predict(self, text):
        words = re.findall(r'\w+', text)
        scores = {c: math.log(self.class_docs[c] / self.total_docs) for c in self.classes}
        for word in words:
            if word not in self.vocab:
                continue
            for c in self.classes:
                freq = self.word_freq[c].get(word, 0)
                scores[c] += math.log((freq + 1) / (sum(self.word_freq[c].values()) + len(self.vocab)))
        return max(scores, key=scores.get)

# 生成情感词典
generate_sentiment_dict(TRAIN_DATA_FILE, SENTIMENT_DICT_FILE)

# 加载情感词典
sentiment_dict = set()
with open(SENTIMENT_DICT_FILE, 'r', encoding='utf-8') as f:
    for line in f:
        word, freq = line.strip().split('\t')
        if int(freq) > 10:  # 过滤掉出现次数过少的词
            sentiment_dict.add(word)

# 训练朴素贝叶斯分类器
classifier = NaiveBayesClassifier()
classifier.train(TRAIN_DATA_FILE)

# 测试朴素贝叶斯分类器
with open(TEST_DATA_FILE, 'r', encoding='utf-8') as f:
    num_correct = 0
    num_total = 0
    for line in f:
        line = line.strip()
        if not line:
            continue
        text, label = line.split('\t')
        pred_label = classifier.predict(text)
        num_correct += int(pred_label == label)
        num_total += 1
    accuracy = num_correct / num_total
    print('Accuracy: {:.2%}'.format(accuracy))

上面的代码实现了以下功能：

1. 从训练集文件中生成情感词典文件。
2. 加载情感词典和训练集文件，训练朴素贝叶斯分类器。
3. 加载测试集文件，测试朴素贝叶斯分类器的准确率。

请注意，这只是一个简单的示例代码，实际上情感分析需要更多的预处理和特征提取步骤。