Qualitative analysis is essential for studying complex social and behavioral phenomena, yet existing large language model (LLM) approaches face key limitations. Fully automated pipelines often compromise methodological rigor, while fully manual coding remains costly and labor-intensive. Although recent work emphasizes human–AI collaboration, existing multi-agent systems focus primarily on theme-level outputs, provide limited human oversight, and overlook fine-grained, data-level coding quality.We introduce CentaurTA, an iterative, self-improving human–agent framework for scalable thematic analysis. CentaurTA places humans in the loop to oversee and guide analysis, using expert feedback as a persistent learning signal to drive prompt-level refinement. By combining structured human feedback with rubric-based evaluation, CentaurTA provides fine-grained supervision for both open coding and theme construction while preserving methodological rigor. Experiments across multiple datasets, baselines, and LLM families show that CentaurTA improves coding alignment and transparency, highlighting the central role of human feedback in reliable qualitative analysis. Our code and data are available at https://github.com/Tom-Owl/CentaurTA.

Individuals engaging in online communication frequently express personal opinions with informal styles (e.g., memes and emojis). While Language Models (LMs) with informal communications have been widely discussed, a unique and emphatic style, the Repetitive Lengthening Form (RLF), has been overlooked for years. In this paper, we explore answers to two research questions: 1) Is RLF important for SA? 2) Can LMs understand RLF? Inspired by previous linguistic research, we curate Lengthening, the first multi-domain dataset with 850k samples focused on RLF for sentiment analysis. Moreover, we introduce Explnstruct, a two-stage Explainable Instruction Tuning framework aimed at improving both the performance and explainability of LLMs for RLF. We further propose a novel unified approach to quantify LMs’ understanding of informal expressions. We show that RLF sentences are expressive expressions and can serve as signatures of document-level sentiment. Additionally, RLF has potential value for online content analysis. Our comprehensive results show that fine-tuned Pre-trained Language Models (PLMs) can surpass zero-shot GPT-4 in performance but not in explanation for RLF. Finally, we show ExpInstruct can improve the open-sourced LLMs to match zero-shot GPT-4 in performance and explainability for RLF with limited samples. Code and sample data are available at https://github.com/Tom-Owl/OverlookedRLF