demesstify 2.0.0

📱Demystifies your messages and allows for easy analysis and visualization of conversations.

Demystify your messages.

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

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Table of contents

• Main features
• Installation
  • Dependencies
• 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
• Generate tailored visualizations such as word clouds or a radial heatmap that plots hour of the day versus 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:

Package	Description
pandas	For easy manipulation of message data
matplotlib	For visualizations
wordcloud	For creating wordclouds
vaderSentiment	For sentiment analysis
calmap	For creating calendar heatmaps
emoji	For working with emojis
lorem	For creating dummy text

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

Authors

• Jake Brehm - Email | Github | LinkedIn