iMessageWrapped was inspired by Spotify's Wrapped feature which allows users to visualize and analyze their listening patterns over the previous year. I wanted to give the same view into texting patterns, analyzing the results of who/how/when/where people had texted.
iMessages and Contacts are stored in a local SQLite database on OSX systems. With user permission, the application accesses both. Contacts have an additional hitch: a user may mantain multiple local contact DBs on the same machine (because, for instance they have imported contacts from multiple devices). As a result, iMessageWrapped loops through all available contact databases, generating a unique list of contacts.
Once connected to the DBs, the application runs queries for the who, what, where, and why sections. Some of my favorite queries were: average time of first text sent during the day, person contacted most after midnight, and top texts by contact. On the frontend, simple visualizations are rendered by passing the the results of these queries directly in a Jinja template. For more complex visualizations (such as texting by time of day or week), I used chart.js and leaflet, which call internal API routes to populate the visualizaitons.
Using nltk, I wrote and trained bigram and trigram models to extract the most frequently used phrases in text conversations. Unfortunately, these increased the distribution size of the application by nearly 200mb and I ultimately decided to scrap them and just include data around the most common usages for figures of speech.
iMessageWrapped was distributed with pyinstaller.