https://github.com/jlumbroso/text-message-analysis-notebook

Jupyter notebook with examples on how to visualize the dataset of personal texts 📱, after extracting from an iPhone with PhoneView.
https://github.com/jlumbroso/text-message-analysis-notebook

jupyter-notebook pandas quantified-self text-message-history text-messages

Last synced: 11 days ago
JSON representation

Jupyter notebook with examples on how to visualize the dataset of personal texts 📱, after extracting from an iPhone with PhoneView.

• Host: GitHub
• URL: https://github.com/jlumbroso/text-message-analysis-notebook
• Owner: jlumbroso
• License: mit
• Created: 2020-08-13T03:30:21.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2020-08-16T03:33:59.000Z (over 4 years ago)
• Last Synced: 2024-11-08T14:38:11.043Z (about 1 month ago)
• Topics: jupyter-notebook, pandas, quantified-self, text-message-history, text-messages
• Language: Jupyter Notebook
• Homepage:
• Size: 906 KB
• Stars: 11
• Watchers: 3
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• project-awesome - jlumbroso/text-message-analysis-notebook - Jupyter notebook with examples on how to visualize the dataset of personal texts 📱, after extracting from an iPhone with PhoneView. (Jupyter Notebook)

README

        
# Text Message Analysis Notebook

[This notebook](Text%20Analysis.ipynb) shows how to visualize the number of text

messages exchanged (with a specific person, or overall), and the volume of characters

exchanged.

![Example of the plot of the number of texts

received/sent with a specific person over time](example-absolute-text-count.png)

![Example of the plot of the rescaled number of texts

received/sent with a specific person over time](example-rescaled-text-count.png)

## Rationale

It is surprising to notice how much information can be extracted from the visualization

of the *patterns* of communication alone—without the contents of the messages. For

instance, in the example above, it is clear that the person sending messages (in green)

is more invested in the relationship: In the *rescaled plot*, we can see that in

the beginning, both of the correspondents are responsible for about 50% of the messages;

but this trend quickly changes over time.

## Requirements

- [PhoneView](https://www.ecamm.com/mac/phoneview/): This is the app I recommend to

extract text messages from your iPhone. It is able to handle SMS, iMessage and WhatsApp

seamlessly. It costs $29.95 for a Lifetime License, and has been available and reliable

since 2008—but there is also a trial version that will work for this.

- Jupyter Notebook and `pandas`: These are the tools I use in this notebook to analyze

and plot my text messages.

### For Windows & Android users

For Windows computers, you can use [iExplorer](https://macroplant.com/iexplorer);

for Android phones, you can use the [SMS Backup and Restore

app](https://play.google.com/store/apps/details?id=com.riteshsahu.SMSBackupRestore&hl=en).

Read [this DigitalTrends article](https://www.digitaltrends.com/mobile/how-to-save-text-messages/)

for more information. The notebook provided here may need to be adapted.