基于屏幕文本情感的情感状态预测
Predicting Affective States from Screen Text Sentiment
AI总结：
本研究利用线性回归和大型语言模型（LLM）的零样本与多样本提示，分析智能手机屏幕文本与情感状态的关系，发现多样本提示表现最佳，为情感预测提供了新方法。
摘要：
移动感知技术的普及使得通过智能手机传感器进行无干扰的数据收集来研究各种生理和行为现象成为可能。这种方法提供了对个体身心状态的实时洞察，为个性化治疗和干预创造了机会。然而，通过分析智能手机上查看的文本内容来预测情感状态的潜力仍未得到充分探索。为了更好地理解用户接触和交互的屏幕文本如何影响其情感，我们调查了2023年针对澳大利亚大学生的一项数字表型研究获取的子集数据。我们采用线性回归、零样本和多样本提示，利用大型语言模型（LLM）来分析屏幕文本与情感状态之间的关系。我们的研究结果表明，多样本提示的表现显著优于线性回归和零样本提示，突出了上下文在情感预测中的重要性。我们讨论了整合文本和情感数据对于改善情感预测的价值，为未来在理解智能手机使用和幸福感方面的进展提供了基础。
英文摘要：
The proliferation of mobile sensing technologies has enabled the study of various physiological and behavioural phenomena through unobtrusive data collection from smartphone sensors. This approach offers real-time insights into individuals' physical and mental states, creating opportunities for personalised treatment and interventions. However, the potential of analysing the textual content viewed on smartphones to predict affective states remains underexplored. To better understand how the screen text that users are exposed to and interact with can influence their affects, we investigated a subset of data obtained from a digital phenotyping study of Australian university students conducted in 2023. We employed linear regression, zero-shot, and multi-shot prompting using a large language model (LLM) to analyse relationships between screen text and affective states. Our findings indicate that multi-shot prompting substantially outperforms both linear regression and zero-shot prompting, highlighting the importance of context in affect prediction. We discuss the value of incorporating textual and sentiment data for improving affect prediction, providing a basis for future advancements in understanding smartphone use and wellbeing.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
