![WhatsApp Group Chat Analysis – [NLP]](https://i0.wp.com/mybraincells.com/wp-content/uploads/2021/11/10981-whatsapp-full-automation-using-python-4-projects-included-806x440-1.jpg?fit=512%2C280&ssl=1)
We analysis WhatsApp group chats using Natural Language Processing
First, download the dataset from here
#Importing the required libraries
import re
import datetime
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from wordcloud import WordCloud, STOPWORDS
import emoji
import itertools
from collections import Counter
import warnings
%matplotlib inline
warnings.filterwarnings(‘ignore’)
#selecting the file and setting formats
file=”whatsapp.txt“
key=”12hr”
split_formats = {
’12hr’ : ‘d{1,2}/d{1,2}/d{2,4},sd{1,2}:d{2}s[APap][mM]s-s’,
’24hr’ : ‘d{1,2}/d{1,2}/d{2,4},sd{1,2}:d{2}s-s’,
‘custom’ : ”
}
datetime_formats = {
’12hr’ : ‘%d/%m/%Y, %I:%M %p – ‘,
’24hr’ : ‘%d/%m/%Y, %H:%M – ‘,
‘custom’: ”
}
#opening and reading a file
with open(file, ‘r’, encoding=’utf-8′) as raw_data:
# converting the list split by newline char. as one whole string as there can be multi-line messages
raw_string = ‘ ‘.join(raw_data.read().split(‘n’))
# splits at all the date-time pattern, resulting in list of all the messages with user names
user_msg = re.split(split_formats[key], raw_string) [1:]
# finds all the date-time patterns
date_time = re.findall(split_formats[key], raw_string)
# finds all the date-time patterns
df = pd.DataFrame({‘date_time’: date_time, ‘user_msg’: user_msg}) # exporting it to a df
# converting date-time pattern which is of type String to type datetime,
# format is to be specified for the whole string where the placeholders are extracted by the method
df[‘date_time’] = pd.to_datetime(df[‘date_time’], format=datetime_formats[key])
# split user and msg
usernames = []
msgs = []
for i in df[‘user_msg’]:
a = re.split(‘([wW]+?):s’, i) # lazy pattern match to first {user_name}: pattern and spliting it aka each msg from a user
if(a[1:]): # user typed messages
usernames.append(a[1])
msgs.append(a[2])
else: # other notifications in the group(eg: someone was added, some left …)
usernames.append(“group_notification”)
msgs.append(a[0])
# creating new columns
df[‘user’] = usernames
df[‘message’] = msgs
# dropping the old user_msg col.
df.drop(‘user_msg’, axis=1, inplace=True)
df
date_time … message
0 2020-01-26 16:19:00 … Messages and calls are end-to-end encrypted. N…
1 2020-01-24 20:25:00 … Tanay Kamath (TSEC, CS) created group “CODERS👨…
2 2020-01-26 16:19:00 … You joined using this group’s invite link
3 2020-01-26 16:20:00 … +91 99871 38558 joined using this group’s invi…
4 2020-01-26 16:20:00 … +91 91680 38866 joined using this group’s invi…
… … … …
13650 2020-10-02 02:05:00 … MCQs mark kiya
13651 2020-10-02 02:05:00 … Sign-in kiya😂😅
13652 2020-10-02 02:11:00 … Incognito se na?
13653 2020-10-02 02:28:00 … Yup
13654 2020-10-02 10:13:00 … guys, please do me a favor and vote in this po…
[13655 rows x 3 columns]
#Checking the info of the df
df.info()
<class ‘pandas.core.frame.DataFrame’>
RangeIndex: 13655 entries, 0 to 13654
Data columns (total 3 columns):
# Column Non-Null Count Dtype
— —— ————– —–
0 date_time 13655 non-null datetime64[ns]
1 user 13655 non-null object
2 message 13655 non-null object
dtypes: datetime64[ns](1), object(2)
memory usage: 320.2+ KB
#Checking the sample of df
df.sample(10)
date_time … message
6322 2020-05-16 12:10:00 … This is really good
2383 2020-02-28 18:34:00 … Nai i was busy
6005 2020-05-09 22:55:00 … Write all numbers not divisible for some n and…
9278 2020-07-30 23:25:00 … 😂😂😂
8705 2020-07-10 17:12:00 … .01 don’t make much difference
790 2020-02-14 10:10:00 … Atleast send the solution for this one
11879 2020-09-13 11:52:00 … Woah 👌🏼🔥
3268 2020-03-15 23:45:00 … Which one?
10586 2020-08-26 12:05:00 … True lol
7677 2020-06-15 17:29:00 … Even I cannot.
[10 rows x 3 columns]
#Checking the shape of the message
df[df[‘message’] == “”].shape[0]
538
#Adding extra helper columns for analysis and visualization
df[‘day’] = df[‘date_time’].dt.strftime(‘%a’)
df[‘month’] = df[‘date_time’].dt.strftime(‘%b’)
df[‘year’] = df[‘date_time’].dt.year
df[‘date’] = df[‘date_time’].apply(lambda x: x.date())
df
date_time user … year date
0 2020-01-26 16:19:00 group_notification … 2020 2020-01-26
1 2020-01-24 20:25:00 group_notification … 2020 2020-01-24
2 2020-01-26 16:19:00 group_notification … 2020 2020-01-26
3 2020-01-26 16:20:00 group_notification … 2020 2020-01-26
4 2020-01-26 16:20:00 group_notification … 2020 2020-01-26
… … … … … …
13650 2020-10-02 02:05:00 Darshan Rander (TSEC, IT) … 2020 2020-10-02
13651 2020-10-02 02:05:00 Darshan Rander (TSEC, IT) … 2020 2020-10-02
13652 2020-10-02 02:11:00 Tanay Kamath (TSEC, CS) … 2020 2020-10-02
13653 2020-10-02 02:28:00 Darshan Rander (TSEC, IT) … 2020 2020-10-02
13654 2020-10-02 10:13:00 Dheeraj Lalwani (TSEC, CS) … 2020 2020-10-02
[13655 rows x 7 columns]
#Copying the file to df1
df1 = df.copy() # I will be using a copy of the original data frame everytime, to avoid loss of data!
df1[‘message_count’] = [1] * df1.shape[0] # adding extra helper column –> message_count.
df1.drop(columns=’year’, inplace=True) # dropping unnecessary columns, using `inplace=True`, since this is copy of the DF and won’t affect the original DataFrame.
df1 = df1.groupby(‘date’).sum().reset_index() # grouping by date; since plot is of frequency of messages –> no. of messages / day.
df1
date message_count
0 2020-01-24 1
1 2020-01-26 105
2 2020-01-27 90
3 2020-01-28 126
4 2020-01-29 118
.. … …
237 2020-09-28 144
238 2020-09-29 49
239 2020-09-30 167
240 2020-10-01 91
241 2020-10-02 22
[242 rows x 2 columns]
#Overall frequency of total messages on the group.
# Improving Default Styles using Seaborn
sns.set_style(“darkgrid”)
# For better readablity;
import matplotlib
matplotlib.rcParams[‘font.size’] = 20
matplotlib.rcParams[‘figure.figsize’] = (27, 6) # Same as `plt.figure(figsize = (27, 6))`
# A basic plot
plt.plot(df1.date, df1.message_count,color=”orange”)
plt.title(‘Messages sent per day over a time period’);
# Could have used Seaborn’s lineplot as well.
sns.lineplot(df1.date, df1.message_count);
# Saving the plots
plt.savefig(‘msg_plots.svg’, format = ‘svg’)
[]
#Checking the trend for last 10days
top10days = df1.sort_values(by=”message_count”, ascending=False).head(10) # Sort values according to the number of messages per day.
top10days.reset_index(inplace=True) # reset index in order.
top10days.drop(columns=”index”, inplace=True) # dropping original indices.
top10days
date message_count
0 2020-09-13 504
1 2020-02-20 379
2 2020-09-20 319
3 2020-08-26 299
4 2020-02-21 278
5 2020-09-12 249
6 2020-08-24 243
7 2020-06-05 233
8 2020-06-12 223
9 2020-02-23 218
#plotting the graph for last 10 days
# Improving Default Styles using Seaborn
sns.set_style(“darkgrid”)
# For better readablity;
import matplotlib
matplotlib.rcParams[‘font.size’] = 10
matplotlib.rcParams[‘figure.figsize’] = (12, 8)
# A bar plot for top 10 days
sns.barplot(top10days.date, top10days.message_count, palette=”hls”);
# Saving the plots
plt.savefig(‘top10_days.svg’, format = ‘svg’)
[]
Top 10 active users on the group
# Total number of people who have sent at least one message on the group;
print(f”Total number of people who have sent at least one message on the group are {len(df.user.unique()) – 1}”) # `-1` because excluding “group_notficiation”
print(f”Number of people who haven’t sent even a single message on the group are {237 – len(df.user.unique()) – 1}”)
Total number of people who have sent at least one message on the group are 154
Number of people who haven’t sent even a single message on the group are 81
df2 = df.copy()
df2 = df2[df2.user != “group_notification”]
top10df = df2.groupby(“user”)[“message”].count().sort_values(ascending=False)
# Final Data Frame
top10df = top10df.head(10).reset_index()
top10df
user message
0 Tanay Kamath (TSEC, CS) 2528
1 Dheeraj Lalwani (TSEC, CS) 1937
2 Darshan Rander (TSEC, IT) 1404
3 Kartik Soneji (TSEC, CS) 841
4 Harsh Kapadia (TSEC IT, SE) 790
5 Pratik K (TSEC CS, SE) 781
6 Saurav Upoor (TSEC CS, SE) 569
7 Tushar Nankani 354
8 +91 82916 21138 275
9 Farhan Irani (TSEC IT, SE) 255
top10df[‘initials’] = ”
for i in range(10):
top10df.initials[i] = top10df.user[i].split()[0][0] + top10df.user[i].split()[1][0]
top10df.initials[7] = “Me” # That’s me
top10df.initials[8] = “DT”
# For better readablity;
import matplotlib
matplotlib.rcParams[‘font.size’] = 14
matplotlib.rcParams[‘figure.figsize’] = (9, 5)
matplotlib.rcParams[‘figure.facecolor’] = ‘#00000000’
# Beautifying Default Styles using Seaborn
sns.set_style(“darkgrid”)
sns.barplot(top10df.initials, top10df.message, data=top10df);
top10df.initials
0 TK
1 DL
2 DR
3 KS
4 HK
5 PK
6 SU
7 Me
8 DT
9 FI
Name: initials, dtype: object
[]
#Most used words in the chat
comment_words = ‘ ‘
stopwords = STOPWORDS.update([‘group’, ‘link’, ‘invite’, ‘joined’, ‘message’, ‘deleted’, ‘yeah’, ‘hai’, ‘yes’, ‘okay’, ‘ok’, ‘will’, ‘use’, ‘using’, ‘one’, ‘know’, ‘guy’, ‘group’, ‘media’, ‘omitted’])
# iterate through the DataFrame.
for val in df3.message.values:
# typecaste each val to string.
val = str(val)
# split the value.
tokens = val.split()
# Converts each token into lowercase.
for i in range(len(tokens)):
tokens[i] = tokens[i].lower()
for words in tokens:
comment_words = comment_words + words + ‘ ‘
wordcloud = WordCloud(width = 600, height = 600,
background_color =’white’,
stopwords = stopwords,
min_font_size = 8).generate(comment_words)
wordcloud.to_image()
[]
