Demystify your messages.

demesstify is a Python library that demystifies your messages and allows for easy analysis and visualization of conversations.

Table of contents

• Main features
• Installation
  • Dependencies
• Documentation
• Example usage
  • Analyzing the messages
  • Creating a word cloud
• Future improvements
• Authors

Main features

Here are just a few things that demesstify can do:

• Read message data from various sources, including your local iMessages database, a Tansee text file, or some randomly generated dummy text
• Perform text analysis on your messages so you can see things like the average number of texts received per day or the most number of messages that were sent in a row
• Analyze which emojis or reactions (if you're using iMessage) were most frequently used, among other thing
• Perform sentiment analysis on your messages to see the polarity of your conversations
• Calculate statistics about the attachments you exchanged (if you're using iMessage)
• Generate tailored visualizations such as word clouds or a radial heatmap that plots hour of the day against day of the week

Installation

demesstify can be installed via pip:

pip install demesstify

The source code can be viewed on GitHub here.

Dependencies

demesstify depends on the following packages:

Documentation

For information on how to use demesstify, please see the documentation.

Example usage

Analyzing the messages

import demesstify as dm
from demesstify.analysis import emojis, sentiment, text

# Create the messages object and dataframes from dummy text
messages = dm.Messages.from_random(total_messages=1000)
all_df = messages.get_all()
sent_df = messages.get_sent()
received_df = messages.get_received()

# Determine the 3 most frequent emojis
most_frequent_emojis = emojis.Emojis(all_df).get_most_frequent(3)

# Determine the total number of messages sent
total_messages_sent = text.Text(sent_df).get_total()

# Determine the average number of messages received per day
average_received_daily = text.Text(received_df).get_average_per_day()

# Determine the number of times "velit" appear as a substring
velit_count = text.Text(all_df).get_count_of_substring('velit')

# Determine the average polarity of the messages
average_polarity = sentiment.Sentiment(all_df).get_average_sentiment()

Creating a word cloud

import demesstify as dm
from demesstify.visualize import cloud

# Create the messages object and dataframes from dummy text
messages = dm.Messages.from_random(total_messages=1000)

# Create and save a Cloud object
wordcloud = cloud.Cloud(messages.as_string('all'))
wordcloud.min_word_length = 3
wordcloud.repeat = False                # will not repeat any words
wordcloud.collocations = False          # will not include pairs of words
wordcloud.include_numbers = False       # will not include numbers
wordcloud.generate()
wordcloud.save('wordcloud.png')

Future improvements

• Add support for other message sources, e.g. Android or social media platforms
• Add unit tests

Authors

• Jake Brehm - Email | Github | LinkedIn