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dataminer.py
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dataminer.py
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import csv
import os
import re
import datetime
import random
from wordcloud import WordCloud
import matplotlib.pyplot as plt
import plotly.graph_objects as go
folder_path = os.getcwd()
data = []
with open("combined.csv", mode='r', encoding='utf-8-sig') as file:
reader = csv.reader(file)
header = next(reader)
for row in reader:
data.append(row)
# All messages ever sent timeline
daily_msg = {}
for message in data:
date = message[0]
author = message[2]
if author not in daily_msg:
daily_msg[author] = {date : 1}
elif date not in daily_msg[author]:
daily_msg[author][date] = 1
else:
daily_msg[author][date] += 1
# Total messages per person
all_word_freq = {}
overall_msg = {}
for message in data:
author = message[2]
msg = message[3]
if author not in overall_msg:
overall_msg[author] = 1
else:
overall_msg[author] += 1
if msg not in all_word_freq:
all_word_freq[msg] = 1
else:
all_word_freq[msg] += 1
# Avg msg per day
daily_avg_msg = {}
days_texting = {}
for person in daily_msg:
person_avg = 0
days = 0
for date in daily_msg[person]:
person_avg += daily_msg[person][date]
days += 1
days_texting[person] = days
person_avg /= days
daily_avg_msg[person] = person_avg
# Avg words per msg
def count_characters_and_words(sentence):
chinese_pattern = re.compile(r'[\u4e00-\u9fff]')
words = sentence.split()
chinese_count = 0
english_word_count = 0
for word in words:
if chinese_pattern.search(word):
chinese_count += len(re.findall(chinese_pattern, word))
else:
english_word_count += 1
return chinese_count, english_word_count
avg_words_per_msg = {}
for message in data:
author = message[2]
msg = message[3]
chn, eng = count_characters_and_words(msg)
total = chn + eng
if author not in avg_words_per_msg:
avg_words_per_msg[author] = total
else:
avg_words_per_msg[author] += total
for person in avg_words_per_msg:
avg_words_per_msg[person] /= overall_msg[person]
# Total photos / videos
total_photos = {}
for message in data:
author = message[2]
msg = message[3]
if msg != "[图片]":
continue
if author not in total_photos:
total_photos[author] = 1
else:
total_photos[author] += 1
# Average hourly messages
total_hourly_msg = {}
avg_hourly_msg = {}
def generateHours():
x = {}
for i in range(0, 24):
x[i] = 0
return x
for message in data:
time = message[1]
author = message[2]
if time == "NA":
continue
hour = int(time.split(':')[0])
if author not in total_hourly_msg:
total_hourly_msg[author] = generateHours()
total_hourly_msg[author][hour] = 1
else:
total_hourly_msg[author][hour] += 1
for person in total_hourly_msg:
avg_hourly_msg[person] = generateHours()
for hour in total_hourly_msg[person]:
avg_hourly_msg[person][hour] = total_hourly_msg[person][hour] / days_texting[person]
# First messages of the day
first_msg = {}
total_first_msg = {}
monthly_first_msg = {}
first_msg_contents = {}
users = list(overall_msg.keys())
for i in range(len(data)-1, -1, -1):
message = data[i]
date = message[0]
month = int(date.split('-')[1])
author = message[2]
msg = ' '.join(message[3].splitlines())
if date not in first_msg:
first_msg[date] = author
if author in total_first_msg:
total_first_msg[author] += 1
else:
total_first_msg[author] = 1
if author not in monthly_first_msg:
monthly_first_msg[author] = {month : 1}
elif month not in monthly_first_msg[author]:
monthly_first_msg[author][month] = 1
else:
monthly_first_msg[author][month] += 1
if msg not in first_msg_contents:
first_msg_contents[msg] = 1
else:
first_msg_contents[msg] += 1
# Average reply time
avg_reply_time = {}
replied_msg = {}
lastMsgAuthor = data[0][2]
for message in data:
time = message[1]
author = message[2]
if time == "NA":
continue
if lastMsgAuthor == author:
lastMsgTime = datetime.datetime.strptime(time, "%H:%M:%S").time()
continue
replyMsgTime = datetime.datetime.strptime(time, "%H:%M:%S").time()
# Create datetime objects with the same date to calculate the time difference
lastMsgDatetime = datetime.datetime.combine(datetime.datetime.today(), lastMsgTime)
replyMsgDatetime = datetime.datetime.combine(datetime.datetime.today(), replyMsgTime)
time_difference_min = abs(lastMsgDatetime - replyMsgDatetime).total_seconds()/60
if author not in replied_msg:
replied_msg[author] = 1
else:
replied_msg[author] += 1
if author not in avg_reply_time:
avg_reply_time[author] = time_difference_min
else:
avg_reply_time[author] += time_difference_min
lastMsgAuthor = author
lastMsgTime = datetime.datetime.strptime(time, "%H:%M:%S").time()
for person in avg_reply_time:
avg_reply_time[person] /= replied_msg[person]
# Sentiment analysis
# I will figure this out someday...
#print("----")
if not os.path.isdir("data"):
os.mkdir("data")
with open("data/daily_msg.csv", 'w', encoding='utf-8-sig', newline='') as file:
writer = csv.writer(file)
headers = []
for x in list(daily_msg.keys()):
headers.append(f"by {x}, x")
headers.append(f"by {x}, y")
writer.writerow(headers)
proc_msg = []
people = list(daily_msg.values())
for x in list(people[0].keys()):
date_list = []
for person in people:
date_list.append(x)
try:
date_list.append(person[x])
except KeyError as k:
#print(k)
pass
proc_msg.append(date_list)
for row in proc_msg:
writer.writerow(row)
with open("data/overall_msg.csv", 'w', encoding='utf-8-sig', newline='') as file:
writer = csv.writer(file)
headers = []
headers.append("Variable")
for x in list(overall_msg.keys()):
headers.append(f"Sent by {x}")
proc_msg = []
total_messages = []
total_messages.append("Total Messages")
for x in list(overall_msg.values()):
total_messages.append(x)
proc_msg.append(total_messages)
msg_per_day = []
msg_per_day.append("Msg/Day")
for x in list(daily_avg_msg.values()):
msg_per_day.append(x)
proc_msg.append(msg_per_day)
awpm = []
awpm.append("Words/Msg")
for x in list(avg_words_per_msg.values()):
awpm.append(x)
proc_msg.append(awpm)
pv = []
pv.append("Photos/Videos")
for x in list(total_photos.values()):
pv.append(x)
proc_msg.append(pv)
rp = []
rp.append("Avg. Reply Time")
for x in list(avg_reply_time.values()):
rp.append(x)
proc_msg.append(rp)
writer.writerow(headers)
for row in proc_msg:
writer.writerow(row)
with open("data/total_hourly_msg.csv", 'w', encoding='utf-8-sig', newline='') as file:
writer = csv.writer(file)
headers = []
headers.append(f"Time of Day")
for x in list(daily_msg.keys()):
headers.append(f"by {x}, y")
writer.writerow(headers)
proc_msg = []
people = list(total_hourly_msg.values())
for x in list(people[0].keys()):
time_list = []
time_list.append(x)
for person in total_hourly_msg:
time_list.append(total_hourly_msg[person][x])
proc_msg.append(time_list)
for row in proc_msg:
writer.writerow(row)
with open("data/avg_hourly_msg.csv", 'w', encoding='utf-8-sig', newline='') as file:
writer = csv.writer(file)
headers = []
headers.append(f"Time of Day")
for x in list(daily_msg.keys()):
headers.append(f"by {x}, y")
writer.writerow(headers)
proc_msg = []
people = list(avg_hourly_msg.values())
for x in list(people[0].keys()):
time_list = []
time_list.append(x)
for person in people:
time_list.append(person[x])
proc_msg.append(time_list)
for row in proc_msg:
writer.writerow(row)
with open("data/first_msg_contents.csv", 'w', encoding='utf-8-sig', newline='') as file:
writer = csv.writer(file)
headers = ["Phrase", "Frequency"]
writer.writerow(headers)
for row in first_msg_contents:
writer.writerow(row)
with open("data/all_word_freq.csv", 'w', encoding='utf-8-sig', newline='') as file:
writer = csv.writer(file)
headers = ["Phrase", "Frequency"]
writer.writerow(headers)
for row in all_word_freq:
# print(row)
writer.writerow([row, all_word_freq[row]])
stop = ["动画表情", "图片"] # I will implement this later
wordcloud = WordCloud(width=3840, height=2160, font_path='HanyiSentyRubber.ttf', colormap='winter', background_color="white").generate_from_frequencies(first_msg_contents)
wordcloud.to_file("data/first_msg_contents.png")
wordcloud = WordCloud(width=3840, height=2160, font_path='HanyiSentyRubber.ttf', colormap='winter',background_color="white").generate_from_frequencies(all_word_freq)
wordcloud.to_file("data/all_msg_contents.png")
from heapq import nlargest
color_scheme = ["#fd7f6f", "#7eb0d5", "#b2e061", "#bd7ebe", "#ffb55a", "#ffee65", "#beb9db", "#fdcce5", "#8bd3c7"]
all_word_freq_tracedata = []
most_frequent_words = nlargest(50, all_word_freq, key=all_word_freq.get)
word_freq = []
for word in most_frequent_words:
word_freq.append(all_word_freq[word])
all_word_freq_trace = go.Bar(
x=list(most_frequent_words),
y=list(word_freq),
marker=dict(
color=random.choice(color_scheme),
)
)
all_word_freq_tracedata.append(all_word_freq_trace)
all_word_freq_bar = go.Figure(
data=all_word_freq_tracedata,
layout_title_text="All word frequency"
)
first_word_freq_tracedata = []
most_frequent_words = nlargest(50, first_msg_contents, key=first_msg_contents.get)
word_freq = []
for word in most_frequent_words:
word_freq.append(first_msg_contents[word])
first_word_freq_trace = go.Bar(
x=list(most_frequent_words),
y=list(word_freq),
marker=dict(
color=random.choice(color_scheme),
)
)
first_word_freq_tracedata.append(first_word_freq_trace)
first_word_freq_bar = go.Figure(
data=first_word_freq_tracedata,
layout_title_text="First message word frequency"
)
total_daily_messages_tracedata = []
for u in users:
c = random.choice(color_scheme)
trace = go.Bar(
x=list(daily_msg[u].keys()),
y=list(daily_msg[u].values()),
name=f'by {u}',
marker=dict(
color=c,
)
)
color_scheme.remove(c)
total_daily_messages_tracedata.append(trace)
total_daily_messages = go.Figure(
data=total_daily_messages_tracedata,
layout_title_text="Total Daily Messages"
)
color_scheme = ["#003f5c","#58508d","#bc5090", "#ff6361", "#ffa600"]
monthly_first_msg_tracedata = []
#print(monthly_first_msg)
for u in users:
if u not in monthly_first_msg:
continue
c = random.choice(color_scheme)
trace = go.Bar(
x=list(monthly_first_msg[u].keys()),
y=list(monthly_first_msg[u].values()),
name=f'by {u}',
marker=dict(
color=c,
)
)
color_scheme.remove(c)
monthly_first_msg_tracedata.append(trace)
monthly_first_message = go.Figure(
data=monthly_first_msg_tracedata,
layout_title_text="Monthly First Message"
)
all_word_freq_bar.show()
first_word_freq_bar.show()
total_daily_messages.show()
monthly_first_message.show()
#print(daily_msg)
#print(overall_msg)
#print(all_word_freq)
#print(daily_avg_msg)
#print(avg_words_per_msg)
#print(total_photos)
#print(total_hourly_msg)
#print(avg_hourly_msg)
#print(days_texting)
#print(users)
#print(first_msg)
#print(total_first_msg)
#print(monthly_first_msg)
#print(first_msg_contents)
#print(replied_msg)
#print(avg_reply_time)