分析iMessage数据，揭示聊天中的话题回避与情绪倾向
Applying NLP to iMessages: Understanding Topic Avoidance, Responsiveness, and Sentiment
- 构建iMessage分析工具，提取消息主题、响应时长与情绪
- 发现用户对敏感话题响应更慢，情绪倾向可量化分析
- 适合研究数字沟通行为或隐私数据应用的学者
我们的iMessage文本消息分析器旨在回答五个核心研究问题：主题建模、响应时间、回避评分和情感分析。通过解析苹果Mac上本地存储的iMessage数据文件（含消息内容及元数据），我们实现了对用户聊天行为的深入探索。研究显示，用户对敏感话题的回应存在明显延迟，且可通过自然语言处理技术量化其情绪倾向。该分析工具为未来iMessage数据研究提供了可行路径，有助于理解短消息通信中的社交动态与心理特征。
原文摘要 · Abstract (English)
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.
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