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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2512.11079 |
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| _version_ | 1866914197029257216 |
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| author | Gerber, Alan Cooperman, Sam |
| author_facet | Gerber, Alan Cooperman, Sam |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_11079 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Applying NLP to iMessages: Understanding Topic Avoidance, Responsiveness, and Sentiment Gerber, Alan Cooperman, Sam Computation and Language Computers and Society Applications Other Statistics J.4; J.5 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. |
| title | Applying NLP to iMessages: Understanding Topic Avoidance, Responsiveness, and Sentiment |
| topic | Computation and Language Computers and Society Applications Other Statistics J.4; J.5 |
| url | https://arxiv.org/abs/2512.11079 |