Chat text analysis

Generate graphs and statistics from your exported Telegram messages.

Usage

First you need to export your Telegram data to a result.json file. You can do this in the settings of the Telegram desktop client.

./telegram-statistics.py -i __import__/result.json -n "name"

Open the file result_2019-05-30.json and parse the chat history with Name Surname starting from 2018-01-01 up to now and generate the substring plot for the emojis "😘💗💙💓🧡😘💕😚😍🥰"

./telegram-statistics.py -i __import__/result_2019-05-30.json -n "Name Surname" -d 2018-01-01 -w "😘;💗;💙;💓;🧡;😘;💕;😚;😍;🥰"

Import Whatsapp

There is a convert-whatsapp.py to import a whatsapp exported Whatsapp Chat with Name.txt into a Telegram style json format. To find the correct [Name Surname] take the name in the first line in the Whatsapp export txt. However, the Whatsapp export is not as detailed as the Telegram export, so many numbers cannot be calculated.

./convert-whatsapp.py -i "Whatsapp Chat with Name.txt"
./telegram-statistics -i __import__/whatsapp-result.json -n "Name Surname"

Where "name" is the name displayed in Telegram (usually the surname).

Generated Files

The script generates multiple files in the __generated__/${person_name}_${person_id} directory

emojis.txt contains unicode encoded emojis and their count
raw_metrics.json raw numerical data (contains all text of both persons / large file)

HTML Files (Plots):

plot_hours.html bokeh plot of message frequency over the hours of one day
plot_month.html bokeh plot of number of messages sent per month
plot_month_characters.html bokeh plot of characters sent per month
plot_weekdays.html bokeh plot of message frequency over one week
plot_month_calls.html bokeh plot of number of calls per month
plot_month_call_time.html bokeh plot of total seconds on call per month
plot_month_photos.html bokeh plot of number of photos sent per month
plot_month_replytime.html bokeh plot of average monthly replytime (Beta)
plot_month_word_occurrence.html bokeh plot of combined substring occurences over time

Raw Files (one for each person):

raw_months_person.csv csv vaues of month data
raw_weekdays_person.csv csv vaues of weekday data
raw_months_chars_person.csv csv vaues of monthly character count data
raw_monthly_pictures_person.csv csv vaues of monthly picture count data
raw_monthly_calls_person.csv csv vaues of monthly number of calls
raw_monthly_call_duration_person.csv csv values of monthly call duration
raw_monthly_time_to_reply_person.csv csv vaues of monthly reply time

Metrics

per chat

total number of messages
total number of words
total number of characters
count occurrence of each word
number of unique words

per person

total number of messages
total number of words
total number of characters
average number of words per message
average number of characters per message
count occurrence of each word
count occurrence of each emoji
number of messages formated with markdown
number of messages of type [animation, audio_file, sticker, video_message, voice_message]
number of photos
number of unique words

Requirements

pip3 install -r requirements.txt

License

MIT License

Based on the original project and modified to my own needs.

kopiro / chat-text-analysis