Applying NLP to iMessages: Understanding Topic Avoidance, Responsiveness, and Sentiment
This work addresses the need for users to understand their own messaging data, but it is incremental as it applies existing NLP methods to a new dataset.
The paper tackled the problem of analyzing personal iMessage data by developing a text message analyzer to answer research questions on topic modeling, response times, reluctance scoring, and sentiment analysis, using locally stored files on Mac to demonstrate its potential for future studies.
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.