zzy5111398
0098977172
1. 修复词云断句问题 - 添加英文单词过滤 2. 创建 Word2Vec + CNN 情绪感知模型 3. 创建情绪时间序列分析脚本（基于大连理工大学情感词典） 4. 添加停用词文件（1427个中英文停用词） 5. 更新 analyze.py 保存时间字段 post_publish_time 6. 更新 requirements.txt 添加必要依赖
297 lines
11 KiB
Python
297 lines
11 KiB
Python
import os
|
||
import json
|
||
import re
|
||
import numpy as np
|
||
import pandas as pd
|
||
from datetime import datetime
|
||
import matplotlib.pyplot as plt
|
||
import matplotlib
|
||
matplotlib.use('Agg')
|
||
import jieba
|
||
|
||
# 设置中文字体
|
||
plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'SimSun', 'Arial Unicode MS']
|
||
plt.rcParams['axes.unicode_minus'] = False
|
||
|
||
# 加载停用词
|
||
def load_stopwords(filepath='stopwords.txt'):
|
||
stopwords = set()
|
||
if os.path.exists(filepath):
|
||
with open(filepath, 'r', encoding='utf-8') as f:
|
||
for line in f:
|
||
word = line.strip()
|
||
if word:
|
||
stopwords.add(word)
|
||
return stopwords
|
||
|
||
STOPWORDS = load_stopwords()
|
||
|
||
# ============================================================
|
||
# 构建情感词典（参照 sentiment_analysis.py）
|
||
# ============================================================
|
||
def build_sentiment_dictionary():
|
||
"""使用大连理工大学中文情感词汇本体构建情感词典"""
|
||
|
||
dict_path = '大连理工大学中文情感词汇本体.xlsx'
|
||
|
||
try:
|
||
df = pd.read_excel(dict_path)
|
||
df = df[['词语', '词性种类', '词义数', '词义序号', '情感分类', '强度', '极性']]
|
||
|
||
Happy = []
|
||
Good = []
|
||
Surprise = []
|
||
Anger = []
|
||
Sad = []
|
||
Fear = []
|
||
Disgust = []
|
||
|
||
for idx, row in df.iterrows():
|
||
if row['情感分类'] in ['PA', 'PE']:
|
||
Happy.append(row['词语'])
|
||
if row['情感分类'] in ['PD', 'PH', 'PG', 'PB', 'PK']:
|
||
Good.append(row['词语'])
|
||
if row['情感分类'] in ['PC']:
|
||
Surprise.append(row['词语'])
|
||
if row['情感分类'] in ['NA']:
|
||
Anger.append(row['词语'])
|
||
if row['情感分类'] in ['NB', 'NJ', 'NH', 'PF']:
|
||
Sad.append(row['词语'])
|
||
if row['情感分类'] in ['NI', 'NC', 'NG']:
|
||
Fear.append(row['词语'])
|
||
if row['情感分类'] in ['NE', 'ND', 'NN', 'NK', 'NL']:
|
||
Disgust.append(row['词语'])
|
||
|
||
# 添加股票相关词汇
|
||
stock_positive = ['涨', '上涨', '暴涨', '拉升', '涨停', '盈利', '收益', '赚钱', '赚',
|
||
'利好', '增长', '上升', '增加', '发展', '进步', '提升', '改善', '突破',
|
||
'创新', '优势', '超预期', '亮眼', '惊艳', '奇迹']
|
||
stock_negative = ['跌', '下跌', '暴跌', '跳水', '跌停', '亏损', '亏钱', '赔', '损失',
|
||
'套牢', '垃圾', '恶心', '坑爹', '骗局', '雷', '爆雷', '崩盘', '退市']
|
||
|
||
Good.extend(stock_positive)
|
||
Disgust.extend(stock_negative)
|
||
|
||
Positive = Happy + Good + Surprise
|
||
Negative = Anger + Sad + Fear + Disgust
|
||
|
||
print(f'大连理工大学情感词典加载完成')
|
||
print(f' 正面情感词: {len(Positive)}个')
|
||
print(f' 负面情感词: {len(Negative)}个')
|
||
|
||
return {
|
||
'Happy': Happy,
|
||
'Good': Good,
|
||
'Surprise': Surprise,
|
||
'Anger': Anger,
|
||
'Sad': Sad,
|
||
'Fear': Fear,
|
||
'Disgust': Disgust,
|
||
'Positive': Positive,
|
||
'Negative': Negative
|
||
}
|
||
|
||
except Exception as e:
|
||
print(f'加载大连理工大学情感词典失败: {e}')
|
||
print('使用简化版情感词典')
|
||
return build_simplified_dictionary()
|
||
|
||
def build_simplified_dictionary():
|
||
"""构建简化的中文情感词典（备用方案）"""
|
||
|
||
Happy = ['开心', '快乐', '高兴', '喜悦', '愉快', '欣喜', '欢乐', '欢喜', '幸福',
|
||
'满意', '满足', '欣慰', '愉悦', '畅快', '乐观', '积极', '美好', '成功']
|
||
|
||
Good = ['好', '优秀', '出色', '精彩', '卓越', '杰出', '优良', '良好', '完美', '不错',
|
||
'涨', '上涨', '暴涨', '拉升', '涨停', '盈利', '收益', '赚钱', '赚', '利好',
|
||
'增长', '上升', '增加', '发展', '进步', '提升', '改善', '突破', '创新', '优势']
|
||
|
||
Surprise = ['惊喜', '意外', '震惊', '惊讶', '震撼', '神奇', '奇迹', '惊艳', '亮眼', '超预期']
|
||
|
||
Anger = ['愤怒', '生气', '恼火', '气愤', '暴怒', '愤慨', '愤恨', '震怒', '发怒',
|
||
'骂', '垃圾', '恶心', '坑爹', '骗局', '欺骗', '欺诈', '造假', '腐败', '黑暗']
|
||
|
||
Sad = ['伤心', '难过', '悲伤', '痛苦', '悲哀', '沮丧', '失望', '绝望', '低落', '悲观',
|
||
'跌', '下跌', '暴跌', '跳水', '跌停', '亏损', '亏钱', '赔', '损失', '套牢']
|
||
|
||
Fear = ['害怕', '恐惧', '担心', '担忧', '恐慌', '不安', '焦虑', '忧虑', '紧张', '恐怖',
|
||
'风险', '危机', '危险', '下跌', '暴跌', '崩盘', '退市', '爆雷', '雷', '怕']
|
||
|
||
Disgust = ['厌恶', '恶心', '反感', '讨厌', '鄙视', '唾弃', '不屑', '蔑视', '嫌弃',
|
||
'垃圾', '废物', '不行', '差劲', '差', '烂', '渣', '骗局']
|
||
|
||
Positive = Happy + Good + Surprise
|
||
Negative = Anger + Sad + Fear + Disgust
|
||
|
||
print(f'简化版情感词典构建完成')
|
||
print(f' 正面情感词: {len(Positive)}个')
|
||
print(f' 负面情感词: {len(Negative)}个')
|
||
|
||
return {
|
||
'Happy': Happy,
|
||
'Good': Good,
|
||
'Surprise': Surprise,
|
||
'Anger': Anger,
|
||
'Sad': Sad,
|
||
'Fear': Fear,
|
||
'Disgust': Disgust,
|
||
'Positive': Positive,
|
||
'Negative': Negative
|
||
}
|
||
|
||
# ============================================================
|
||
# 情绪计算函数（参照 sentiment_analysis.py）
|
||
# ============================================================
|
||
def emotion_caculate(text, sentiment_dict):
|
||
"""计算单条文本的情绪"""
|
||
|
||
if not text or pd.isna(text):
|
||
return 0
|
||
|
||
positive = 0
|
||
negative = 0
|
||
|
||
wordlist = jieba.lcut(text)
|
||
|
||
for word in wordlist:
|
||
# 跳过停用词和短词
|
||
if word in STOPWORDS or len(word) <= 1:
|
||
continue
|
||
|
||
freq = wordlist.count(word)
|
||
|
||
if word in sentiment_dict['Positive']:
|
||
positive += freq
|
||
if word in sentiment_dict['Negative']:
|
||
negative += freq
|
||
|
||
sentiment_score = positive - negative
|
||
return sentiment_score
|
||
|
||
# ============================================================
|
||
# 时间序列分析
|
||
# ============================================================
|
||
def analyze_sentiment_trend():
|
||
"""分析情绪时间序列趋势（使用情感词典）"""
|
||
print("="*60)
|
||
print("情绪时间序列分析（基于情感词典）")
|
||
print("="*60)
|
||
|
||
# 构建情感词典
|
||
print("\n[1/5] 构建情感词典...")
|
||
sentiment_dict = build_sentiment_dictionary()
|
||
|
||
# 加载数据
|
||
print("\n[2/5] 加载数据...")
|
||
df = pd.read_csv('output/all_posts.csv', encoding='utf-8-sig')
|
||
|
||
# 检查是否有 post_publish_time 字段
|
||
if 'post_publish_time' not in df.columns:
|
||
print("警告：数据中没有 post_publish_time 字段，请先运行 analyze.py")
|
||
return
|
||
|
||
# 转换时间戳
|
||
print("\n[3/5] 转换时间戳...")
|
||
df['timestamp'] = pd.to_datetime(df['post_publish_time'], errors='coerce')
|
||
df = df.dropna(subset=['timestamp'])
|
||
df['date'] = df['timestamp'].dt.date
|
||
|
||
# 计算情绪得分
|
||
print("\n[4/5] 计算情绪得分...")
|
||
df['sentiment_score'] = df['clean_text'].apply(
|
||
lambda x: emotion_caculate(x, sentiment_dict)
|
||
)
|
||
|
||
# 保存结果
|
||
df.to_csv('output/sentiment_analysis_result.csv', index=False, encoding='utf-8-sig')
|
||
print(" 情绪分析结果已保存到: output/sentiment_analysis_result.csv")
|
||
|
||
# 按股票分组分析
|
||
stock_groups = df.groupby('stock_code')
|
||
os.makedirs('output/plots', exist_ok=True)
|
||
|
||
print("\n[5/5] 生成时间序列图表...")
|
||
for stock_code, group in stock_groups:
|
||
stock_name = group['stock_name'].iloc[0]
|
||
print(f"\n 分析 {stock_name} ({stock_code})...")
|
||
|
||
# 按日期分组计算平均情绪
|
||
daily_sentiment = group.groupby('date')['sentiment_score'].agg(['mean', 'count']).reset_index()
|
||
daily_sentiment.columns = ['date', 'avg_sentiment', 'post_count']
|
||
|
||
if len(daily_sentiment) < 2:
|
||
print(f" 数据不足，跳过")
|
||
continue
|
||
|
||
# 绘制时间序列图
|
||
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(12, 8), sharex=True)
|
||
|
||
# 情绪趋势
|
||
ax1.plot(daily_sentiment['date'], daily_sentiment['avg_sentiment'],
|
||
marker='o', linestyle='-', color='b', label='日均情绪')
|
||
|
||
# 添加移动平均线
|
||
daily_sentiment['MA3'] = daily_sentiment['avg_sentiment'].rolling(window=3).mean()
|
||
ax1.plot(daily_sentiment['date'], daily_sentiment['MA3'],
|
||
marker='', linestyle='--', color='r', label='3日移动平均')
|
||
|
||
ax1.set_title(f'{stock_name} ({stock_code}) 情绪时间序列趋势', fontsize=14)
|
||
ax1.set_ylabel('情绪分数', fontsize=12)
|
||
ax1.axhline(y=0, color='gray', linestyle='-', linewidth=0.5)
|
||
ax1.grid(True)
|
||
ax1.legend()
|
||
|
||
# 发帖量
|
||
ax2.bar(daily_sentiment['date'], daily_sentiment['post_count'], color='g', alpha=0.7)
|
||
ax2.set_xlabel('日期', fontsize=12)
|
||
ax2.set_ylabel('发帖数量', fontsize=12)
|
||
ax2.grid(True)
|
||
|
||
plt.xticks(rotation=45)
|
||
plt.tight_layout()
|
||
|
||
# 保存图表
|
||
plot_path = f'output/plots/sentiment_trend_{stock_name}.png'
|
||
plt.savefig(plot_path, dpi=100)
|
||
plt.close()
|
||
print(f" 图表已保存到: {plot_path}")
|
||
|
||
# 输出统计信息
|
||
avg_sentiment = group['sentiment_score'].mean()
|
||
pos_count = (group['sentiment_score'] > 0).sum()
|
||
neg_count = (group['sentiment_score'] < 0).sum()
|
||
neu_count = (group['sentiment_score'] == 0).sum()
|
||
print(f" 平均情绪: {avg_sentiment:.4f}")
|
||
print(f" 正面帖子: {pos_count}, 负面帖子: {neg_count}, 中性帖子: {neu_count}")
|
||
|
||
# 生成汇总报告
|
||
print("\n生成汇总报告...")
|
||
summary_data = []
|
||
for stock_code, group in stock_groups:
|
||
stock_name = group['stock_name'].iloc[0]
|
||
avg_sentiment = group['sentiment_score'].mean()
|
||
post_count = len(group)
|
||
pos_count = (group['sentiment_score'] > 0).sum()
|
||
neg_count = (group['sentiment_score'] < 0).sum()
|
||
neu_count = (group['sentiment_score'] == 0).sum()
|
||
|
||
summary_data.append({
|
||
'股票代码': stock_code,
|
||
'股票名称': stock_name,
|
||
'帖子数量': post_count,
|
||
'平均情绪': round(avg_sentiment, 4),
|
||
'正面帖子': pos_count,
|
||
'负面帖子': neg_count,
|
||
'中性帖子': neu_count
|
||
})
|
||
|
||
summary_df = pd.DataFrame(summary_data)
|
||
summary_df.to_csv('output/sentiment_summary.csv', index=False, encoding='utf-8-sig')
|
||
print("汇总报告已保存到: output/sentiment_summary.csv")
|
||
|
||
print("\n" + "="*60)
|
||
print("情绪时间序列分析完成！")
|
||
print("="*60)
|
||
|
||
if __name__ == '__main__':
|
||
analyze_sentiment_trend() |