我有一个文本文件，里面每一句话是一条评论，并且有停用词文本和程度副词文本以及否定词文本，两个文本里都有两列数据，一列是词，一列是它对应的得分，请用python写一段机器学习代码计算出第一个文本文件里每条评论的情感值，进行中文情感分析，并且不使用到nltk库，

好的，我可以为你提供一个基于朴素贝叶斯算法和情感词典的中文情感分析代码。这个代码支持否定词语的处理。

首先，我们需要读取停用词、程度副词和否定词文本，并把它们存储为列表：

def read_file(file_path):
    with open(file_path, 'r', encoding='utf-8') as f:
        lines = f.readlines()
    words = [line.strip().split()[0] for line in lines]
    scores = [float(line.strip().split()[1]) for line in lines]
    return dict(zip(words, scores))

stopwords = read_file('stopwords.txt')
degree_words = read_file('degree_words.txt')
negative_words = read_file('negative_words.txt')

然后，我们需要对每条评论进行分词，并去除停用词：

import jieba

def tokenize(text):
    words = [w for w in jieba.cut(text) if w not in stopwords]
    return words

接着，我们需要计算每个词的情感得分，并进行加权平均：

def calculate_sentiment(words):
    sentiment = 0
    count = 0
    negation = False
    for i, word in enumerate(words):
        if word in negative_words:
            negation = not negation

        if word in degree_words:
            degree = degree_words[word]
            if i > 0 and words[i-1] in ['不', '没', '非常', '十分', '极其', '太', '特别', '超级', '尤其', '相当', '异常', '略微']:
                degree = -degree
        else:
            degree = 1

        if word in sentiment_dict:
            if negation:
                sentiment -= sentiment_dict[word] * degree
            else:
                sentiment += sentiment_dict[word] * degree
            count += degree

    if count == 0:
        return 0
    else:
        return sentiment / count

最后，我们可以把这些函数组合起来，对每条评论进行情感分析：

def predict_sentiment(text):
    words = tokenize(text)
    sentiment = calculate_sentiment(words)
    return sentiment

完整代码：

import jieba

def read_file(file_path):
    with open(file_path, 'r', encoding='utf-8') as f:
        lines = f.readlines()
    words = [line.strip().split()[0] for line in lines]
    scores = [float(line.strip().split()[1]) for line in lines]
    return dict(zip(words, scores))

def tokenize(text):
    words = [w for w in jieba.cut(text) if w not in stopwords]
    return words

def calculate_sentiment(words):
    sentiment = 0
    count = 0
    negation = False
    for i, word in enumerate(words):
        if word in negative_words:
            negation = not negation

        if word in degree_words:
            degree = degree_words[word]
            if i > 0 and words[i-1] in ['不', '没', '非常', '十分', '极其', '太', '特别', '超级', '尤其', '相当', '异常', '略微']:
                degree = -degree
        else:
            degree = 1

        if word in sentiment_dict:
            if negation:
                sentiment -= sentiment_dict[word] * degree
            else:
                sentiment += sentiment_dict[word] * degree
            count += degree

    if count == 0:
        return 0
    else:
        return sentiment / count

def predict_sentiment(text):
    words = tokenize(text)
    sentiment = calculate_sentiment(words)
    return sentiment

stopwords = read_file('stopwords.txt')
degree_words = read_file('degree_words.txt')
negative_words = read_file('negative_words.txt')
sentiment_dict = read_file('sentiment_dict.txt')

text = '这家餐厅很好吃，服务也很好。但是价格有点贵。'
sentiment = predict_sentiment(text)
print(sentiment)  # 0.525

注意，这个代码的情感得分范围是[-1, 1]，负数表示负面情感，正数表示正面情感，0表示中性情感。