以下是重构后的代码：
```python
import jieba
import jieba.posseg as pseg
import pandas as pd
import numpy as np
import re
import codecs
from sklearn.externals import joblib
# 加载情感词典
posdict = []
negdict = []
with codecs.open('posdict.txt', 'r', 'utf-8') as f:
for line in f.readlines():
posdict.append(line.strip())
with codecs.open('negdict.txt', 'r', 'utf-8') as f:
for line in f.readlines():
negdict.append(line.strip())
# 加载情感分类模型
clf = joblib.load('svm_model.pkl')
def analyze_hlm():
# 加载红楼梦文本
with codecs.open('hongloumeng.txt', 'r', 'utf-8') as f:
text = f.read()
# 对红楼梦进行情感分析
words = pseg.cut(text)
pos_count = [(word, flag) for word, flag in words if flag in posdict]
neg_count = [(word, flag) for word, flag in words if flag in negdict]
pos_num = len(pos_count)
neg_num = len(neg_count)
pos_rate = pos_num / (pos_num + neg_num) if pos_num + neg_num > 0 else 0
neg_rate = neg_num / (pos_num + neg_num) if pos_num + neg_num > 0 else 0
pos_result = "正面情感" if pos_rate >= 0.5 else "中性情感" if pos_rate > 0 else "负面情感" if pos_rate < 0 else "无法判断"
neg_result = "负面情感" if neg_rate >= 0.5 else "中性情感" if neg_rate > 0 else "正面情感" if neg_rate < 0 else "无法判断"
return pos_result, neg_result
```
文本进行分词和词性标注：
```python
import jieba.posseg as pseg
import numpy as np
from sklearn.svm import SVC
# 加载情感词典和正面词汇、负面词汇的字典文件
posdict = {} # 请在此处补充正面词汇的字典，例如：{"积极": 0}
negdict = {} # 请在此处补充负面词汇的字典，例如：{"消极": 0}
text = "请在这里输入需要分析的文本"
words = pseg.cut(text) # 提取文本中的名词和动词
nouns = []
verbs = []
for w in words:
if w.flag.startswith('n'):
nouns.append(w.word)
elif w.flag.startswith('v'):
verbs.append(w.word)
# 统计文本中积极和消极情感词的数量
pos_count = len([word for word in nouns + verbs if word in posdict])
neg_count = len([word for word in nouns + verbs if word in negdict])
# 构建特征向量
X = np.array((pos_count, neg_count)).reshape(1, -1)
# 加载情感分类器模型(请根据实际情况选择合适的模型并进行训练)
clf = SVC()
pred = clf.predict(X)
# 输出情感分析结果
if pred == 1:
print("红楼梦整体情感倾向为积极。")
elif pred == -1:
print("红楼梦整体情感倾向为消极。")
else:
print("红楼梦整体情感倾向为中性。")
```
注意：以上代码仅为示例，实际情感分析过程会更加复杂，需要根据具体情况进行调整和优化。
