Modeling dimensional affect in longitudinal text requires distinguishing current affect estimation from future affective change forecasting. Existing approaches often treat each text as an independent observation and apply similar assumptions to both tasks, without testing whether they rely on different information sources. This paper investigates that distinction using longitudinal self-reported ecological essays and feeling-word entries. We propose the Trait--State Affective Prediction (TSAP) framework and its temporal extension E-TSAP for per-text valence and arousal prediction, evaluated on a held-out prediction test set of 1,737 entries from 91 users. We further propose the Affective Change Forecaster Hybrid (ACF-Hybrid) for next-step affective change forecasting, evaluated on a held-out forecasting test set of 46 users. For prediction, E-TSAP achieves composite Pearson correlations of 0.670 for valence and 0.449 for arousal. For forecasting, textual representations perform worse than compact numeric trajectory baselines: the text-inclusive model achieves only r=0.316 for valence and r=0.284 for arousal, whereas a simple prior-state baseline reaches r=0.615 and r=0.670, respectively. ACF-Hybrid, using dimension-specific numeric trajectory features, achieves r=0.659 for valence and $r=0.658$ for arousal. These results show that textual semantics support current affect prediction, whereas future affective change is better captured through prior numeric trajectory dynamics.

翻译：建模纵向文本中的维度情感需要区分当前情感估计与未来情感变化预报。现有方法通常将每个文本视为独立观测值，并对两项任务应用相似假设，但未检验它们是否依赖不同信息源。本文利用纵向自我报告生态短文和感受词条目研究这一区别。我们提出特质-状态情感预测（TSAP）框架及其时间扩展模型E-TSAP，用于逐篇文本的效价和唤醒度预测，并在包含91名用户1737条条目的保留预测测试集上进行评估。我们进一步提出情感变化预报混合模型（ACF-Hybrid）用于下一步情感变化预报，并在包含46名用户的保留预报测试集上进行评估。在预测任务中，E-TSAP在效价和唤醒度上分别达到0.670和0.449的复合皮尔逊相关系数。在预报任务中，文本表征的表现逊于紧凑数值轨迹基线：包含文本的模型在效价上仅为r=0.316，在唤醒度上为r=0.284，而简单先验状态基线分别达到r=0.615和r=0.670。采用维度特异性数值轨迹特征的ACF-Hybrid在效价上达到r=0.659，在唤醒度上达到r=0.658。这些结果表明，文本语义支持当前情感预测，而未来情感变化则更有效地通过先验数值轨迹动态捕捉。