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import matplotlib.pyplot as plt
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import matplotlib.font_manager as fm
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from wordcloud import WordCloud
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import pandas as pd
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import numpy as np
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from collections import Counter
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import os
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class Visualizer:
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def __init__(self):
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# 设置中文字体
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plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'DejaVu Sans']
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plt.rcParams['axes.unicode_minus'] = False
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self.font_path = self.find_chinese_font()
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def find_chinese_font(self):
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"""寻找中文字体"""
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try:
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# 尝试常见的中文字体路径
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font_paths = [
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'C:/Windows/Fonts/simhei.ttf', # Windows
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'/System/Library/Fonts/PingFang.ttc', # macOS
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'/usr/share/fonts/truetype/droid/DroidSansFallbackFull.ttf' # Linux
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]
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for font_path in font_paths:
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if os.path.exists(font_path):
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return font_path
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# 如果找不到，使用matplotlib默认字体
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return None
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except:
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return None
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def create_wordcloud(self, word_freq_df: pd.DataFrame, save_path: str):
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"""创建词云图"""
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# 创建词频字典
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word_freq = dict(zip(word_freq_df['词语'], word_freq_df['频次']))
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# 创建词云
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wc_config = {
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'width': 1200,
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'height': 800,
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'background_color': 'white',
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'colormap': 'viridis',
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'max_words': 100,
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'relative_scaling': 0.5
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}
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if self.font_path:
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wc_config['font_path'] = self.font_path
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wc = WordCloud(**wc_config)
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wordcloud = wc.generate_from_frequencies(word_freq)
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# 绘制词云
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plt.figure(figsize=(15, 10))
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plt.imshow(wordcloud, interpolation='bilinear')
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plt.axis('off')
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plt.title('大语言模型应用弹幕词云分析', fontsize=20, pad=20)
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plt.tight_layout()
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# 确保目录存在
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os.makedirs('visualization', exist_ok=True)
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plt.savefig(save_path, dpi=300, bbox_inches='tight',
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facecolor='white', edgecolor='none')
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plt.show()
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print(f"词云图已保存到: {save_path}")
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def plot_applications_bar(self, top_apps_df: pd.DataFrame, save_path: str):
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"""绘制应用领域条形图"""
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plt.figure(figsize=(12, 8))
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colors = plt.cm.Set3(np.linspace(0, 1, len(top_apps_df)))
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bars = plt.barh(top_apps_df['应用领域'], top_apps_df['出现次数'],
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color=colors, edgecolor='black', alpha=0.8)
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# 添加数据标签
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for bar in bars:
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width = bar.get_width()
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plt.text(width + 0.1, bar.get_y() + bar.get_height()/2,
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f'{int(width)}', ha='left', va='center', fontsize=12)
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plt.xlabel('出现次数', fontsize=14)
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plt.title('大语言模型应用领域分布（Top 8）', fontsize=16, pad=20)
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plt.grid(axis='x', alpha=0.3)
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plt.tight_layout()
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plt.savefig(save_path, dpi=300, bbox_inches='tight')
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plt.show()
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print(f"应用领域分布图已保存到: {save_path}")
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def plot_sentiment_analysis(self, processed_df: pd.DataFrame, save_path: str):
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"""绘制情感分析图"""
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# 简单的情感关键词分类
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positive_words = ['好', '强', '棒', '厉害', '方便', '高效', '智能', '强大', '优秀', '推荐']
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negative_words = ['差', '弱', '问题', '担心', '风险', '贵', '难', '复杂', '取代', '改进']
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sentiment_counts = {'积极': 0, '消极': 0, '中性': 0}
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for danmu in processed_df['original_danmu']:
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positive_count = sum(1 for word in positive_words if word in danmu)
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negative_count = sum(1 for word in negative_words if word in danmu)
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if positive_count > negative_count:
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sentiment_counts['积极'] += 1
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elif negative_count > positive_count:
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sentiment_counts['消极'] += 1
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else:
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sentiment_counts['中性'] += 1
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# 绘制饼图
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plt.figure(figsize=(10, 8))
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colors = ['#ff9999', '#66b3ff', '#99ff99']
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plt.pie(sentiment_counts.values(), labels=sentiment_counts.keys(),
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autopct='%1.1f%%', colors=colors, startangle=90,
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explode=(0.1, 0, 0)) # 突出显示积极评价
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plt.title('弹幕情感倾向分布', fontsize=16)
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plt.savefig(save_path, dpi=300, bbox_inches='tight')
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plt.show()
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print(f"情感分析图已保存到: {save_path}")
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def create_comprehensive_visualization(self, processed_df: pd.DataFrame,
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top_apps_df: pd.DataFrame,
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word_freq_df: pd.DataFrame):
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"""创建综合可视化"""
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# 确保可视化目录存在
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os.makedirs('visualization', exist_ok=True)
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# 1. 词云图
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self.create_wordcloud(word_freq_df, 'visualization/wordcloud.png')
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# 2. 应用领域分布
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self.plot_applications_bar(top_apps_df, 'visualization/applications_distribution.png')
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# 3. 情感倾向分析
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self.plot_sentiment_analysis(processed_df, 'visualization/sentiment_analysis.png')
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def main():
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visualizer = Visualizer()
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try:
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# 加载处理后的数据
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processed_df = pd.read_excel('data/processed/llm_analysis.xlsx',
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sheet_name='弹幕数据')
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top_apps_df = pd.read_excel('data/processed/llm_analysis.xlsx',
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sheet_name='应用领域排名')
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word_freq_df = pd.read_excel('data/processed/llm_analysis.xlsx',
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sheet_name='词频统计')
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# 创建可视化
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visualizer.create_comprehensive_visualization(processed_df, top_apps_df, word_freq_df)
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print("所有可视化图表生成完成！")
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except Exception as e:
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print(f"可视化过程中出现错误: {e}")
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print("请先运行 data_processor.py 生成数据")
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if __name__ == "__main__":
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main() |
