最近在网上看到别人做的爬取微信聊天记录并分析聊天内容，GitHub上试着运行了一下，这好东西肯定要分享出来给各位，总结一下几年的微信聊天内容，废话不多说，下面一步步来。先展示一下，我和我对象的聊天内容分析：

源代码和出处：GitHub – LC044/WeChatMsg: 提取微信聊天记录，将其导出成HTML、Word、CSV文档永久保存，对聊天记录进行分析生成年度聊天报告

大家记得给作者点点star，督促作者开发更优的信息抓取功能。

一、微信聊天记录爬取

下载微信聊天记录爬取程序：（软件安全正常，直接无视安全问题）

https://github.com/LC044/WeChatMsg/releases/download/v1.0.6/MemoTrace-1.0.6.exe

电脑需要登录微信，如果电脑微信聊天记录不齐全，可以通过手机进行微信聊天记录迁移。

  • 安卓： 手机微信->我->设置->聊天->聊天记录迁移与备份->迁移->迁移到电脑微信（迁移完成后重启微信）
  • iOS： 手机微信->我->设置->通用->聊天记录迁移与备份->迁移->迁移到电脑微信（迁移完成后重启微信）

打开软件，随后点击获取信息，获取手机号、微信昵称、wxid等内容，之后点击开始启动就行。

若出现wxid或微信路径无法获取问题，查看解决办法（”留痕”使用教程 (lc044.love)），一般都是没问题的。

选择 “数据 –> 批量导出”，选择你想要导出的联系人信息。导出格式选择csv格式，方便我们后续利用python进行数据分析：

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导出后的结果在程序同目录下的“data –> 聊天记录“文件中，我们需要csv文件，记住csv文件的地址，自此微信聊天记录爬取结束。

PS：上述软件也可以进行数据分析，作者也贴出年度报告，各位可以尝试一下，不过内容较少且存在乱码。

二、内容分析可视化展示：

环境配置：python3.8（3.10matplotlib不兼容问题） numpy pandas seaborn jieba july wordcloud

接下来直接内容分析代码，代码中需要根据你的CSV文件地址修改以及聊天双方名字修改：

import matplotlib.pyplot as pltimport pandas as pdimport reimport julyimport jiebafrom july.utils import date_rangeimport seaborn as snsfrom scipy.stats import normimport numpy as npfrom wordcloud import WordCloudfrom collections import Counterdef set_chinese_font():# 设置中文字体plt.rcParams['font.sans-serif'] = ['SimHei']# 设置中文字体为黑体plt.rcParams['axes.unicode_minus'] = False# 用来正常显示负号def read_chat_data(file_path):# 读取CSV文件df = pd.read_csv(file_path)return dfdef preprocess_data(df):# 数据预处理df = df[df['Type'] == 1]# 只保留文本聊天selected_columns = ['IsSender', 'StrContent', 'StrTime']df = df[selected_columns]# 只取'IsSender','StrContent','StrTime'列df['StrTime'] = pd.to_datetime(df['StrTime'])df['Date'] = df['StrTime'].dt.datereturn dfdef plot_chat_frequency_by_day(df):# 每天聊天频率柱状图chat_frequency = df['Date'].value_counts().sort_index()chat_frequency.plot(kind='bar', color='#DF9F9B')total_messages = len(df)date_labels = [date.strftime('%m-%d') for date in chat_frequency.index]plt.text(30, 1300, '消息总数：{0}条'.format(total_messages), ha='left', va='top', fontsize=10, color='black')plt.text(30, 1250, '起止时间：{0} --- {1}'.format(date_labels[0], date_labels[-1]), ha='left', va='top', fontsize=10, color='black')plt.xlabel('Date')plt.ylabel('Frequency')plt.title('Chat Frequency by Day')plt.xticks(range(1, len(date_labels), 7), date_labels[::7])plt.xticks(fontsize=5)plt.show()def plot_calendar_heatmap(df):# 制作日历热力图df['Date'] = pd.to_datetime(df['Date'])start_date = df['Date'].min()end_date = df['Date'].max()dates = date_range(start_date, end_date)july.heatmap(dates=dates, data=df['Date'].value_counts().sort_index(), cmap='Pastel1', month_grid=True, horizontal=True, value_label=False, date_label=False, weekday_label=True, month_label=True, year_label=True, colorbar=False, fontfamily="monospace", fontsize=12, title=None, titlesize='large', dpi=100)plt.tight_layout()plt.show()def analyze_message_comparison(df):# 双方信息数量对比sent_by_me = df[df['IsSender'] == 1]['StrContent']sent_by_others = df[df['IsSender'] == 0]['StrContent']count_sent_by_me = len(sent_by_me)count_sent_by_others = len(sent_by_others)labels = ['你的名字', '聊天对象的名字']sizes = [count_sent_by_me, count_sent_by_others]colors = ['#FF6347', '#9ACD32']explode = (0, 0.05)plt.rc('font', family='YouYuan')plt.pie(sizes, explode=explode, labels=labels, colors=colors, autopct='%1.1f%%', shadow=True, startangle=90)plt.axis('equal')plt.title('Comparison of the number of chats')plt.legend()plt.show()def analyze_hourly_chat_frequency(df):# 根据一天中的每一个小时进行统计聊天频率，并生成柱状图df['DateTime'] = pd.to_datetime(df['StrTime'])df['Hour'] = df['DateTime'].dt.hourhourly_counts = df['Hour'].value_counts().sort_index().reset_index()hourly_counts.columns = ['Hour', 'Frequency']plt.figure(figsize=(10, 8))plt.rc('font', family='YouYuan')ax = sns.barplot(x='Hour', y='Frequency', data=hourly_counts, color="#E6AAAA")sns.kdeplot(df['Hour'], color='#C64F4F', linewidth=1, ax=ax.twinx())plt.title('Chat Frequency by Hour')plt.xlabel('Hour of the Day')plt.ylabel('Frequency')plt.show()def is_chinese_word(word):for char in word:if not re.match(r'[\u4e00-\u9fff]', char):return Falsereturn Truedef correct(a, stop_words):b = []for word in a:if len(word) > 1 and is_chinese_word(word) and word not in stop_words:b.append(word)return bdef word_fre_draw(a, str):a_counts = Counter(a)top_30_a = a_counts.most_common(30)words, frequencies = zip(*top_30_a)# 绘制水平柱状图plt.figure(figsize=(10, 15))plt.barh(words, frequencies, color='skyblue')plt.xlabel('Frequency')plt.ylabel('Words')plt.title('Top 30 Words in Chat Messages for {0}'.format(str))plt.show()def word_frequency_analysis(df):sent_by_me_text = ' '.join(df[df['IsSender'] == 1]['StrContent'].astype(str))sent_by_others_text = ' '.join(df[df['IsSender'] == 0]['StrContent'].astype(str))all_text = ' '.join(df['StrContent'].astype(str))words = list(jieba.cut(all_text, cut_all=False))my_words = list(jieba.cut(sent_by_me_text, cut_all=False))others_words = list(jieba.cut(sent_by_others_text, cut_all=False))with open('stopwords_hit.txt', encoding='utf-8') as f:# 添加屏蔽词汇con = f.readlines()stop_words = set()# 集合可以去重for i in con:i = i.replace("\n", "")# 去掉读取每一行数据的\nstop_words.add(i)Words = correct(words, stop_words)My_words = correct(my_words, stop_words)others_words = correct(others_words, stop_words)words_space_split = ' '.join(Words)word_fre_draw(Words, 'All')word_fre_draw(My_words, '你的名字')word_fre_draw(others_words, '他/她的名字')return words_space_splitdef word_cloud(words_space_split):wordcloud = WordCloud(font_path='‪C:\Windows\Fonts\STCAIYUN.TTF',width=800, height=600,background_color='white',max_words=200,max_font_size=100,).generate(words_space_split)plt.figure(figsize=(10, 8))plt.imshow(wordcloud, interpolation='bilinear')plt.axis('off')plt.show()def analyze_weekly_contribution(df):df['Weekday'] = df['StrTime'].dt.day_name()# 计算每天的消息数量weekday_counts = df['Weekday'].value_counts().reindex(["Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday"])# 找出频率最高的那天max_day = weekday_counts.idxmax()# 制作饼状图plt.figure(figsize=(8, 8))explode = [0.1 if day == max_day else 0 for day in weekday_counts.index]# 突出显示频率最高的那天plt.pie(weekday_counts, labels=weekday_counts.index, explode=explode, autopct='%1.1f%%',startangle=140, colors=plt.cm.Paired.colors)plt.title('Distribution of Messages During the Week')plt.show()def analyze_most_active_day_and_month(df):df['Date'] = pd.to_datetime(df['Date'])df['YearMonth'] = df['Date'].dt.to_period('M')df['Day'] = df['Date'].dt.datedaily_counts = df['Day'].value_counts()max_day = daily_counts.idxmax()max_day_count = daily_counts.max()monthly_counts = df['YearMonth'].value_counts()max_month = monthly_counts.idxmax()max_month_count = monthly_counts.max()print(f"Most active day: {max_day}, with {max_day_count} messages.")print(f"Most active month: {max_month}, with {max_month_count} messages.")if __name__ == "__main__":set_chinese_font()df = read_chat_data('CSV文件')# 加载数据集df = preprocess_data(df)# 数据预处理plot_chat_frequency_by_day(df)# 绘制每日聊天频率柱状图plot_calendar_heatmap(df)# 绘制日历热力图analyze_message_comparison(df)# 消息占比对比analyze_hourly_chat_frequency(df)# 每小时聊天频率柱状图words = word_frequency_analysis(df)# 词汇频率分析word_cloud(words)# 词云制作analyze_weekly_contribution(df)# 每周聊天频率analyze_most_active_day_and_month(df)# 聊天最多的月和天

文件中引用有停词文件，可以从GitHub上下载你想使用的（差不多都一样，可以在文件中添加新的屏蔽词语）。停词文件和代码文件放在同一目录下：

GitHub – goto456/stopwords: 中文常用停用词表（哈工大停用词表、百度停用词表等）

然后直接运行代码就可以等着一张一张的图片展示啦

各位有任何问题评论区欢迎提问