Applying NLP to iMessages: Understanding Topic Avoidance, Responsiveness, and Sentiment

TL;DR

This paper introduces an NLP-based analyzer for iMessage data that explores topic avoidance, responsiveness, and sentiment, providing insights into user communication patterns and potential privacy implications.

Contribution

It presents a novel tool for analyzing iMessage data, addressing key communication behaviors and sentiment analysis within Apple's messaging ecosystem.

Findings

Identifies patterns in topic avoidance and responsiveness.

Provides sentiment analysis of iMessage conversations.

Demonstrates potential for future research using iMessage data.

Abstract

What is your messaging data used for? While many users do not often think about the information companies can gather based off of their messaging platform of choice, it is nonetheless important to consider as society increasingly relies on short-form electronic communication. While most companies keep their data closely guarded, inaccessible to users or potential hackers, Apple has opened a door to their walled-garden ecosystem, providing iMessage users on Mac with one file storing all their messages and attached metadata. With knowledge of this locally stored file, the question now becomes: What can our data do for us? In the creation of our iMessage text message analyzer, we set out to answer five main research questions focusing on topic modeling, response times, reluctance scoring, and sentiment analysis. This paper uses our exploratory data to show how these questions can be answered using our analyzer and its potential in future studies on iMessage data.