Python代码出现NameError：'stop_words'未定义问题排查

错误根源 #

你在preprocess_text函数内部定义的stop_words是局部变量，仅在函数执行范围内有效，函数外部的代码（比如生成词云的部分）无法访问这个变量，因此触发NameError。

修复步骤 #

1. 将停用词定义改为全局变量 #

把stop_words的定义移到函数外部，让整个代码都能调用：

# 全局定义停用词集合
stop_words = set(stopwords.words("english"))
stop_words.update(["a", "an", "and", "are", "as", "at", "be", "by", 
    "for", "from", "has", "he", "in", "is", "it", "its", "of", "on", 
    "that", "the", "to", "was", "were", "will", "with"])

# 预处理函数（直接使用全局的stop_words）
def preprocess_text(text):
    text = text.lower()
    text = re.sub(r'\[^\\w\\s\]', '', text)
    words = word_tokenize(text)
    words = [word for word in words if word not in stop_words]
    lemmatizer = WordNetLemmatizer()
    words = [lemmatizer.lemmatize(word) for word in words]
    return words

2. 修正缩进错误 #

你统计正负特征的循环里存在缩进问题，会导致语法错误，需要调整：

positive_features = {}
negative_features = {}
for i in range(len(comments)):
    comment = comments[i]
    sentiment = sentiments[i]
    words = preprocess_text(comment)
    for word in words:
        # 此处需缩进，与外层for对齐
        if sentiment > 0:
            if word in positive_features:
                positive_features[word] += 1
            else:
                positive_features[word] = 1
        elif sentiment < 0:
            if word in negative_features:
                negative_features[word] += 1
            else:
                negative_features[word] = 1

3. 词云部分正常使用全局stop_words #

修改后生成词云的代码就能正常调用stop_words，无需额外改动。

额外优化说明 #

将停用词设为全局变量，不仅解决了变量访问问题，还避免了每次调用预处理函数时重复创建相同的集合，提升了代码效率，同时保证函数内外使用的停用词完全一致，逻辑更统一。

内容的提问来源于stack exchange，提问作者cody