411 lines
18 KiB
Python
411 lines
18 KiB
Python
# -*- 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)
|