Traditional approaches to semantic polarity in computational linguistics treat sentiment as a unidimensional scale, overlooking the multidimensional structure of language. This work introduces TOPol (Topic-Orientation POLarity), a semi-unsupervised framework for reconstructing and interpreting multidimensional narrative polarity fields under human-on-the-loop (HoTL) defined contextual boundaries (CBs). The framework embeds documents using a transformer-based large language model (tLLM), applies neighbor-tuned UMAP projection, and segments topics via Leiden partitioning. Given a CB between discourse regimes A and B, TOPol computes directional vectors between corresponding topic-boundary centroids, yielding a polarity field that quantifies fine-grained semantic displacement during regime shifts. This vectorial representation enables assessing CB quality and detecting polarity changes, guiding HoTL CB refinement. To interpret identified polarity vectors, the tLLM compares their extreme points and produces contrastive labels with estimated coverage. Robustness analyses show that only CB definitions (the main HoTL-tunable parameter) significantly affect results, confirming methodological stability. We evaluate TOPol on two corpora: (i) U.S. Central Bank speeches around a macroeconomic breakpoint, capturing non-affective semantic shifts, and (ii) Amazon product reviews across rating strata, where affective polarity aligns with NRC valence. Results demonstrate that TOPol consistently captures both affective and non-affective polarity transitions, providing a scalable, generalizable, and interpretable framework for context-sensitive multidimensional discourse analysis.

翻译：计算语言学中传统的语义极性方法将情感视为单维标度，忽视了语言的多维结构。本文提出TOPol（面向主题的极性），一种半无监督框架，用于在人在回路（HoTL）定义的语境边界（CBs）下重建和解释多维叙事极性场。该框架使用基于Transformer的大语言模型（tLLM）嵌入文档，应用邻域调谐的UMAP投影，并通过Leiden分区进行主题分割。给定话语体系A与B之间的CB，TOPol计算相应主题边界质心间的方向向量，生成量化体系转换期间细粒度语义位移的极性场。这种向量表示能够评估CB质量并检测极性变化，指导HoTL CB的优化。为解释识别的极性向量，tLLM比较其极值点并生成带有估计覆盖度的对比标签。鲁棒性分析表明，仅CB定义（主要的HoTL可调参数）显著影响结果，证实了方法的稳定性。我们在两个语料库上评估TOPol：（i）围绕宏观经济断点的美国央行演讲，捕捉非情感性语义转变；（ii）跨评分层级的亚马逊产品评论，其中情感极性与NRC效价一致。结果表明，TOPol能一致捕捉情感与非情感极性转换，为语境敏感的多维话语分析提供了一个可扩展、可泛化且可解释的框架。