QQ聊天记录简单分析

$0^.$ Description

  从QQ导出了和好友从2016-08-25到2017-11-18的消息记录，85874行，也算不少。于是就有了大致分析、可视化一下。步骤大致如下：

• 消息记录文件预处理
• 使用jieba分词
• 使用wordcloud生成词云
• 生成简单图表

  结果大致如下：

$1^.$ Preprocessing

  导出的文件大概格式如下：（已去掉多余空行）

2016-08-26 11:02:56 PM 少平
这……
2016-08-26 11:03:02 PM 少平
这bug都被你发现了
2016-08-26 11:03:04 PM C
反驳呀
2016-08-26 11:03:25 PM C
too young
2016-08-26 11:04:43 PM C
我去刷鞋子
2016-08-26 11:04:58 PM 少平
嗯嗯
好的

Observation&Notice：

• 每条消息上都有对应发送时间和发送者
• 列表内容
• 一条消息内可能有换行

  由此，

• 可以依照发送者对消息分开为聊天双方。
• 将各自的内容分别放在文件中，便于后续分词和制作词云。
• 将所有聊天时间抽取出来，可以对聊天时段进行分析和图表绘制。

Arguments:
  $infile\Rightarrow$原始导出消息记录文件
  $outfile1\Rightarrow$对话一方的消息记录文件名
  $outfile2\Rightarrow$对话另一方的消息记录文件名
Outputs:
  预处理后的分别储存的消息记录文件(其中只包含一方聊天内容)以及一个消息时间文件

# -*- coding: utf-8 -*-
""" Spilt the original file into different types in good form. """import re
import codecsIN_FILE = './data.txt'
OUT_CONTENT_FILE_1 = './her_words.txt'
OUT_CONTENT_FILE_2 = './my_words.txt'
OUT_TIME_FILE = './time.txt'
UTF8='utf-8'
MY_NAME_PATTERN = u'少平'
TIME_PATTERN = r'\d{4,4}-\d\d-\d\d \d{1,2}:\d\d:\d\d [AP]M'
TEST_TPYE_LINE = u'2017-10-14 1:13:49 AM 少平'def split(infile, outfile1, outfile2):"""Spilt the original file into different types in good form."""out_content_file_1 = codecs.open(outfile1, 'a', encoding=UTF8)out_content_file_2 = codecs.open(outfile2, 'a', encoding=UTF8)out_time_file = codecs.open(OUT_TIME_FILE, 'a', encoding=UTF8)try:with codecs.open(infile, 'r', encoding=UTF8) as infile:line = infile.readline().strip()while line:if re.search(TIME_PATTERN, line) is not None: # type linestime = re.search(TIME_PATTERN, line).group()out_time_file.write(u'{}\n'.format(time))content_line = infile.readline()flag = 0    # stands for my wordsif re.search(MY_NAME_PATTERN, line):flag = 0else:flag = 1while content_line and re.search(TIME_PATTERN, content_line) is None:if flag == 1:out_content_file_1.write(content_line)else:out_content_file_2.write(content_line)content_line = infile.readline()line = content_lineexcept OSError:print 'error occured here.'out_time_file.close()out_content_file_1.close()out_content_file_2.close()if __name__ == '__main__':split(IN_FILE, OUT_CONTENT_FILE_1, OUT_CONTENT_FILE_2)

$2^.$ Get word segmentations using jieba

  使用jieba分词对聊天记录进行分词。

import codecs
import jiebaIN_FILE_NAME = ('./her_words.txt', './my_words.txt')
OUT_FILE_NAME = ('./her_words_out.txt', './my_words_out.txt')def split(in_files, out_files):"""Cut the lines into segmentations and save to files"""for in_file, out_file in zip(in_files, out_files):outf = codecs.open(out_file, 'a', encoding=UTF8)with codecs.open(in_file, 'r', encoding=UTF8) as inf:line = inf.readline()while line:line = line.strip()seg_list = jieba.cut(line, cut_all=True, HMM=True)for word in seg_list:outf.write(word+'\n')line = inf.readline()outf.close()if __name__ == '__main__':split(IN_FILE_NAME, OUT_FILE_NAME)

$3^.$ Make wordclouds using wordcloud

  抽取分词结果中出现频率最高的120个词，使用wordcloud进行词云生成。并且，屏蔽部分词语(STOP_WORDS)，替换部分词语(ALTER_WORDS)。

STOP_WORDS = [u'图片', u'表情', u'窗口', u'抖动', u'我要', u'小姐', u'哈哈哈', u'哈哈哈哈', u'啊啊啊', u'嘿嘿嘿']
ALTER_WORDS = {u'被替换词1':u'替换词1',u'被替换词2':u'替换词2'}

Arguments:
  $in\_files \Rightarrow$分词产生的结果文件
  $out\_files \Rightarrow$保存词云的目标地址
  $shape\_files \Rightarrow$词云形状的图片文件
Output:
  对话双方各自内容的词云

import jieba.analyse
import numpy as np
from PIL import Image
from wordcloud import WordCloudOUT_FILE_NAME = ('./her_words_out.txt', './my_words_out.txt')
OUT_IMG_NAME = ('./her_wordcloud.png', './my_wordcloud.png')
SHAPE_IMG_NAME = ('./YRY.png', './FBL.png')def make_wordcould(in_files, out_files, shape_files):"""make wordcould"""for in_file, out_file, shape_file in zip(in_files, out_files, shape_files):shape = np.array(Image.open(shape_file))content = codecs.open(in_file, 'r', encoding=UTF8).read()tags = jieba.analyse.extract_tags(content, topK=120, withWeight=True)text = {}for word, freq in tags:if word not in STOP_WORDS:if word in ALTER_WORDS:word = ALTER_WORDS[word]text[word] = freqwordcloud = WordCloud(background_color='white', font_path='./font.ttf', mask=shape, width=1080, height=720).generate_from_frequencies(text)wordcloud.to_file(out_file)if __name__ == '__main__':make_wordcould(OUT_FILE_NAME, OUT_IMG_NAME, SHAPE_IMG_NAME)

以下是指定的词云形状(对应WordCloud()中的mask参数)：

以下是生成的词云：

$4^.$ Generate a simple bar plot about time

  根据预处理中产生的时间文件制作简单柱状图。

#-*- coding: utf-8 -*-
""" make a simple bar plot """import codecs
import matplotlib.pyplot as pltFILE = 'time.txt'def make_bar_plot(file_name):"""make a simple bar plot"""time_list = {}message_cnt = 1with codecs.open(file_name, 'r', encoding='utf-8') as infile:line = infile.readline()while line:line = line.strip()time_in_12, apm = line.split()[1:2]time_in_24 = time_format(time_in_12, apm)if time_in_24 in time_list:time_list[time_in_24] = time_list[time_in_24] + 1else:time_list[time_in_24] = 1line = infile.readline()message_cnt = message_cnt + 1plt.figure(figsize=(18, 9))plt.bar(time_list.keys(), time_list.values(), width=.8,facecolor='lightskyblue', edgecolor='white')plt.xticks(range(len(time_list)), time_list.keys())for x_axies in time_list:y_axies = time_list[x_axies]label = '{}%'.format(round(y_axies*1.0/message_cnt*100, 2))plt.text(x_axies, y_axies+0.05, label, ha='center', va='bottom')plt.title('#message in each hour')plt.savefig('time.png')def time_format(time_in_12, apm):"""docstring"""hour = time_in_12.split(':')[0]hour = int(hour)if apm == 'PM':hour = hour + 12time_in_24 = hour % 24return time_in_24if __name__ == '__main__':make_bar_plot(FILE)

生成的柱状图如下：