# -*- coding: utf-8 -*-# # ------------------------------------------------------------------------------- # Name: analyser.py # Description: # Author: xaoyaoo # Date: 2023/12/01 # ------------------------------------------------------------------------------- import sqlite3 import time from collections import Counter from pywxdump.db.utils import xml2dict from pywxdump.db import dbMSG def date_chat_count(chat_data, interval="W"): """ 获取每个时间段的聊天数量 :param chat_data: 聊天数据 json {"CreateTime":时间,"Type":消息类型,"SubType":消息子类型,"StrContent":消息内容,"StrTalker":聊天对象,"IsSender":是否发送者} :param interval: 时间间隔 可选值：day、month、year、week """ import pandas as pd chat_data = pd.DataFrame(chat_data) chat_data["CreateTime"] = pd.to_datetime(chat_data["CreateTime"]) chat_data["AdjustedTime"] = pd.to_datetime(chat_data["CreateTime"]) - pd.Timedelta(hours=4) chat_data["AdjustedTime"] = chat_data["AdjustedTime"].dt.strftime("%Y-%m-%d %H:%M:%S") chat_data["CreateTime"] = chat_data["CreateTime"].dt.strftime("%Y-%m-%d %H:%M:%S") interval_dict = {"day": "%Y-%m-%d", "month": "%Y-%m", "year": "%Y", "week": "%Y-%W", "d": "%Y-%m-%d", "m": "%Y-%m", "y": "%Y", "W": "%Y-%W" } if interval not in interval_dict: raise ValueError("interval参数错误，可选值为day、month、year、week") chat_data["interval"] = chat_data["AdjustedTime"].dt.strftime(interval_dict[interval]) # 根据chat_data["interval"]最大值和最小值，生成一个时间间隔列表 interval_list = pd.date_range(chat_data["AdjustedTime"].min(), chat_data["AdjustedTime"].max(), freq=interval) interval_list = interval_list.append(pd.Index([interval_list[-1] + pd.Timedelta(days=1)])) # 最后一天加一天 # 构建数据集 # interval type_name1 type_name2 type_name3 # 2021-01 文本数量 其他类型数量 其他类型数量 # 2021-02 文本数量 其他类型数量 其他类型数量 type_data = pd.DataFrame(columns=["interval"] + list(chat_data["type_name"].unique())) type_data["interval"] = interval_list.strftime(interval_dict[interval]) type_data = type_data.set_index("interval") for type_name in chat_data["type_name"].unique(): type_data[type_name] = chat_data[chat_data["type_name"] == type_name].groupby("interval").size() type_data["全部类型"] = type_data.sum(axis=1) type_data["发送"] = chat_data[chat_data["IsSender"] == 1].groupby("interval").size() type_data["接收"] = chat_data[chat_data["IsSender"] == 0].groupby("interval").size() return type_data def read_msgs(MSG_path, selected_talker=None, start_time=time.time() * 3600 * 24 * 365, end_time=time.time()): """ 读取消息内容-MSG.db 包含IsSender，StrContent，StrTalker，ype，SubType，CreateTime，MsgSvrID :param MSG_path: MSG.db 路径 :param selected_talker: 选中的聊天对象 :param start_time: 开始时间 时间戳10位 :param end_time: 结束时间 时间戳10位 :return: """ type_name_dict = { 1: {0: "文本"}, 3: {0: "图片"}, 34: {0: "语音"}, 43: {0: "视频"}, 47: {0: "动画表情"}, 49: {0: "文本", 1: "类文本消息", 5: "卡片式链接", 6: "文件", 8: "上传的GIF表情", 19: "合并转发聊天记录", 33: "分享的小程序", 36: "分享的小程序", 57: "带有引用的文本", 63: "视频号直播或回放等", 87: "群公告", 88: "视频号直播或回放等", 2000: "转账消息", 2003: "红包封面"}, 50: {0: "语音通话"}, 10000: {0: "系统通知", 4: "拍一拍", 8000: "系统通知"} } # 连接 MSG_ALL.db 数据库，并执行查询 db1 = sqlite3.connect(MSG_path) cursor1 = db1.cursor() if isinstance(start_time, str): start_time = time.mktime(time.strptime(start_time, "%Y-%m-%d %H:%M:%S")) if isinstance(end_time, str): end_time = time.mktime(time.strptime(end_time, "%Y-%m-%d %H:%M:%S")) if selected_talker is None or selected_talker == "": # 如果 selected_talker 为 None，则查询全部对话 cursor1.execute( "SELECT MsgSvrID,IsSender, StrContent, StrTalker, Type, SubType,CreateTime FROM MSG WHERE CreateTime>=? AND CreateTime<=? ORDER BY CreateTime ASC", (start_time, end_time)) else: cursor1.execute( "SELECT MsgSvrID,IsSender, StrContent, StrTalker, Type, SubType,CreateTime FROM MSG WHERE StrTalker=? AND CreateTime>=? AND CreateTime<=? ORDER BY CreateTime ASC", (selected_talker, start_time, end_time)) result1 = cursor1.fetchall() cursor1.close() db1.close() def get_emoji_cdnurl(row): if row["type_name"] == "动画表情": parsed_content = xml2dict(row["StrContent"]) if isinstance(parsed_content, dict) and "emoji" in parsed_content: return parsed_content["emoji"].get("cdnurl", "") return row["content"] init_data = pd.DataFrame(result1, columns=["MsgSvrID", "IsSender", "StrContent", "StrTalker", "Type", "SubType", "CreateTime"]) init_data["CreateTime"] = pd.to_datetime(init_data["CreateTime"], unit="s") init_data["AdjustedTime"] = init_data["CreateTime"] - pd.Timedelta(hours=4) init_data["AdjustedTime"] = init_data["AdjustedTime"].dt.strftime("%Y-%m-%d %H:%M:%S") init_data["CreateTime"] = init_data["CreateTime"].dt.strftime("%Y-%m-%d %H:%M:%S") init_data["type_name"] = init_data.apply(lambda x: type_name_dict.get(x["Type"], {}).get(x["SubType"], "未知"), axis=1) init_data["content"] = init_data.apply(lambda x: x["StrContent"] if x["type_name"] == "文本" else "", axis=1) init_data["content"] = init_data.apply(get_emoji_cdnurl, axis=1) init_data["content_len"] = init_data.apply(lambda x: len(x["content"]) if x["type_name"] == "文本" else 0, axis=1) chat_data = init_data[ ["MsgSvrID", "IsSender", "StrTalker", "type_name", "content", "content_len", "CreateTime", "AdjustedTime"]] return True, chat_data # 绘制直方图 def draw_hist_all_count(chat_data, out_path="", is_show=False): try: import matplotlib.pyplot as plt except ImportError as e: print("error", e) raise ImportError("请安装matplotlib库") plt.rcParams['font.sans-serif'] = ['SimHei'] plt.rcParams['axes.unicode_minus'] = False type_count = Counter(chat_data["type_name"]) # 对type_count按值进行排序，并返回排序后的结果 sorted_type_count = dict(sorted(type_count.items(), key=lambda item: item[1], reverse=True)) plt.figure(figsize=(12, 8)) plt.bar(range(len(sorted_type_count)), list(sorted_type_count.values()), tick_label=list(sorted_type_count.keys())) plt.title("消息类型分布图") plt.xlabel("消息类型") plt.ylabel("数量") # 设置x轴标签的旋转角度为45度 plt.xticks(rotation=-45) # 在每个柱上添加数字标签 for i, v in enumerate(list(sorted_type_count.values())): plt.text(i, v, str(v), ha='center', va='bottom') if out_path != "": plt.savefig(out_path) if is_show: plt.show() plt.close() # 按照interval绘制折线图 def draw_line_type_name(chat_data, interval="W", type_name_list=None, out_path="", is_show=False): """ 绘制折线图，横轴为时间，纵轴为消息数量，不同类型的消息用不同的颜色表示 :param chat_data: :param interval: :param type_name_list: 消息类型列表，按照列表中的顺序绘制折线图 可选：全部类型、发送、接收、总字数、发送字数、接收字数、其他类型 :param out_path: :param is_show: :return: """ if type_name_list is None: type_name_list = ["全部类型", "发送", "接收"] + ["总字数", "发送字数", "接收字数"] # type_name_list = ["总字数", "发送字数", "接收字数"] try: import matplotlib.pyplot as plt import pandas as pd except ImportError as e: print("error", e) raise ImportError("请安装matplotlib库") plt.rcParams['font.sans-serif'] = ['SimHei'] plt.rcParams['axes.unicode_minus'] = False chat_data["CreateTime"] = pd.to_datetime(chat_data["CreateTime"]) chat_data["AdjustedTime"] = pd.to_datetime(chat_data["AdjustedTime"]) # interval = interval.lower() interval_dict = {"day": "%Y-%m-%d", "month": "%Y-%m", "year": "%Y", "week": "%Y-%W", "d": "%Y-%m-%d", "m": "%Y-%m", "y": "%Y", "W": "%Y-%W" } if interval not in interval_dict: raise ValueError("interval参数错误，可选值为day、month、year、week") chat_data["interval"] = chat_data["AdjustedTime"].dt.strftime(interval_dict[interval]) # 根据chat_data["interval"]最大值和最小值，生成一个时间间隔列表 interval_list = pd.date_range(chat_data["AdjustedTime"].min(), chat_data["AdjustedTime"].max(), freq=interval) interval_list = interval_list.append(pd.Index([interval_list[-1] + pd.Timedelta(days=1)])) # 最后一天加一天 # 构建数据集 # interval type_name1 type_name2 type_name3 # 2021-01 文本数量 其他类型数量 其他类型数量 # 2021-02 文本数量 其他类型数量 其他类型数量 type_data = pd.DataFrame(columns=["interval"] + list(chat_data["type_name"].unique())) type_data["interval"] = interval_list.strftime(interval_dict[interval]) type_data = type_data.set_index("interval") for type_name in chat_data["type_name"].unique(): type_data[type_name] = chat_data[chat_data["type_name"] == type_name].groupby("interval").size() type_data["全部类型"] = type_data.sum(axis=1) type_data["发送"] = chat_data[chat_data["IsSender"] == 1].groupby("interval").size() type_data["接收"] = chat_data[chat_data["IsSender"] == 0].groupby("interval").size() type_data["总字数"] = chat_data.groupby("interval")["content_len"].sum() type_data["发送字数"] = chat_data[chat_data["IsSender"] == 1].groupby("interval")["content_len"].sum() type_data["接收字数"] = chat_data[chat_data["IsSender"] == 0].groupby("interval")["content_len"].sum() type_data = type_data.fillna(0) # 调整typename顺序，使其按照总数量排序，只要最大的5个 type_data = type_data.reindex(type_data.sum().sort_values(ascending=False).index, axis=1) if type_name_list is not None: type_data = type_data[type_name_list] else: type_data = type_data.iloc[:, :5] # if interval == "W" or interval == "week": # 改为当前周的周一的日期 # # plt.figure(figsize=(12, 8)) # 绘制折线图 for type_name in type_data.columns: plt.plot(type_data.index, type_data[type_name], label=type_name) # 设置x轴标签的旋转角度为45度 plt.xticks(rotation=-45) # 设置标题、坐标轴标签、图例等信息 plt.title("消息类型分布图") plt.xlabel("时间") plt.ylabel("数量") plt.legend(loc="upper right") # 设置图例位置 # 显示图形 if out_path != "": plt.savefig(out_path) if is_show: plt.tight_layout() plt.show() plt.close() def wordcloud_generator(chat_data, interval="m", stopwords=None, out_path="", is_show=False, bg_img=None, font="C:\Windows\Fonts\simhei.ttf"): """ 词云 :param is_show: 是否显示 :param img_path: 背景图片路径 :param text: 文本 :param font: 字体路径 :return: """ try: from wordcloud import WordCloud, ImageColorGenerator import wordcloud import jieba import numpy as np import matplotlib.pyplot as plt from matplotlib.font_manager import fontManager import pandas as pd import codecs import re from imageio import imread except ImportError as e: print("error", e) raise ImportError("请安装wordcloud,jieba,numpy,matplotlib,pillow库") plt.rcParams['font.sans-serif'] = ['SimHei'] plt.rcParams['axes.unicode_minus'] = False chat_data["CreateTime"] = pd.to_datetime(chat_data["CreateTime"]) chat_data["AdjustedTime"] = pd.to_datetime(chat_data["AdjustedTime"]) # interval = interval.lower() interval_dict = {"day": "%Y-%m-%d", "month": "%Y-%m", "year": "%Y", "week": "%Y-%W", "d": "%Y-%m-%d", "m": "%Y-%m", "y": "%Y", "W": "%Y-%W" } if interval not in interval_dict: raise ValueError("interval参数错误，可选值为day、month、year、week") chat_data["interval"] = chat_data["AdjustedTime"].dt.strftime(interval_dict[interval]) # 根据chat_data["interval"]最大值和最小值，生成一个时间间隔列表 interval_list = pd.date_range(chat_data["AdjustedTime"].min(), chat_data["AdjustedTime"].max(), freq=interval) interval_list = interval_list.append(pd.Index([interval_list[-1] + pd.Timedelta(days=1)])) # 最后一天加一天 # 构建数据集 # interval text_all text_sender text_receiver # 2021-01 文本\n合并 聊天记录\n文本\n合并 聊天记录\n文本\n合并 聊天记录\n def merage_text(x): pattern = re.compile("(\[.+?\])") # 匹配表情 rt = "\n".join(x) rt = pattern.sub('', rt).replace("\n", " ") return rt chat_data["content"] = chat_data.apply(lambda x: x["content"] if x["type_name"] == "文本" else "", axis=1) text_data = pd.DataFrame(columns=["interval", "text_all", "text_sender", "text_receiver"]) text_data["interval"] = interval_list.strftime(interval_dict[interval]) text_data = text_data.set_index("interval") # 使用“\n”合并 text_data["text_all"] = chat_data.groupby("interval")["content"].apply(merage_text) text_data["text_sender"] = chat_data[chat_data["IsSender"] == 1].groupby("interval")["content"].apply(merage_text) text_data["text_receiver"] = chat_data[chat_data["IsSender"] == 0].groupby("interval")["content"].apply(merage_text) def gen_img(texts,out_path,is_show,bg_img,title=""): words = jieba.lcut(texts) res = [word for word in words if word not in stopwords and word.replace(" ", "") != "" and len(word) > 1] count_dict = dict(Counter(res)) if bg_img: bgimg = imread(open(bg_img, 'rb')) # 获得词云对象，设定词云背景颜色及其图片和字体 wc = WordCloud(background_color='white', mask=bgimg, font_path='simhei.ttf', mode='RGBA', include_numbers=False, random_state=0) else: # 如果你的背景色是透明的，请用这两条语句替换上面两条 bgimg = None wc = WordCloud(background_color='white', mode='RGBA', font_path='simhei.ttf', include_numbers=False, random_state=0,width=500, height=500) # 如果不指定中文字体路径，词云会乱码 wc = wc.fit_words(count_dict) fig = plt.figure(figsize=(8, 8)) fig.suptitle(title, fontsize=26) ax = fig.subplots() ax.imshow(wc) ax.axis('off') if out_path != "": plt.savefig(out_path) if is_show: plt.show() plt.close() for i in text_data.index: out_path = f"out/img_{i}.png" gen_img(text_data["text_all"][i], out_path=out_path, is_show=False, bg_img=bg_img, title=f"全部({i})") # gen_img(text_data["text_sender"][i], out_path="", is_show=is_show, bg_img=bg_img, title=f"发送_{i}") # gen_img(text_data["text_receiver"][i], out_path="", is_show=is_show, bg_img=bg_img, title=f"接收_{i}") # time.sleep(1) # 情感分析 def sentiment_analysis(chat_data, stopwords="", out_path="", is_show=False, bg_img=None): try: from snownlp import SnowNLP import pandas as pd import matplotlib.pyplot as plt import seaborn as sns except ImportError as e: print("error", e) raise ImportError("请安装snownlp,pandas,matplotlib,seaborn库") sns.set_style('white', {'font.sans-serif': ['simhei', 'FangSong']}) chats = [] for row in chat_data: if row["type_name"] != "文本" or row["content"] == "": continue chats.append(row) scores = [] for row in chats: s = SnowNLP(row["content"]) scores.append(s.sentiments) def draw(data): df = pd.DataFrame({'Sentiment Score': data}) plt.figure(figsize=(8, 6)) sns.histplot(data=df, x='Sentiment Score', kde=True) plt.title("Sentiment Analysis") plt.xlabel("Sentiment Score") plt.ylabel("Frequency") if out_path != "": plt.savefig(out_path) if is_show: plt.show() plt.close() draw(scores) if __name__ == '__main__': MSG_PATH = r"" selected_talker = "wxid_" start_time = time.time() - 3600 * 24 * 50000 end_time = time.time() code, chat_data = read_msgs(MSG_PATH, selected_talker, start_time, end_time) # print(chat_data) # code, data, classify_count, all_type_count = merge_chat_data(chat_data, interval="month") # draw_hist_all_count(chat_data, is_show=True) # 绘制直方图 消息类型分布图 # draw_line_type_name(chat_data, is_show=True) # 绘制折线图 消息类型分布图 # bg_img = 'img.png' stopwords = ['的', '了', '是', '在', '我', '有', '和', '就', '不', '人', '都', '一', '一个', '上', '也', '很', '到', '说', '要', '去', '你', '会', '着', '没有', '看', '好', '自己', '这'] wordcloud_generator(chat_data, stopwords=stopwords, out_path="", is_show=True) # sentiment_analysis(chat_data)