The explosion of big social data has created a scalability trap for traditional qualitative research, as manual coding remains labor-intensive and conventional topic models often suffer from semantic thinning and a lack of domain awareness. This paper introduces Textual Hybrid Embedding based Topic Analysis (THETA), a novel computational paradigm and open-source tool designed to bridge the gap between massive data scale and rich theoretical depth. THETA moves beyond frequency-based statistics by implementing Domain-Adaptive Fine-tuning (DAFT) via LoRA on foundation embedding models, which effectively optimizes semantic vector structures within specific social contexts to capture latent meanings. To ensure epistemological rigor, we encapsulate this process into an AI Scientist Agent framework, comprising Data Steward, Modeling Analyst, and Domain Expert agents, to simulate the human-in-the-loop expert judgment and constant comparison processes central to grounded theory. Departing from purely computational models, this framework enables agents to iteratively evaluate algorithmic clusters, perform cross-topic semantic alignment, and refine raw outputs into logically consistent theoretical categories. To validate the effectiveness of THETA, we conducted experiments across six domains, including financial regulation and public health. Our results demonstrate that THETA significantly outperforms traditional models, such as LDA, ETM, and CTM, in capturing domain-specific interpretive constructs while maintaining superior coherence. By providing an interactive analysis platform, THETA democratizes advanced natural language processing for social scientists and ensures the trustworthiness and reproducibility of research findings. Code is available at https://github.com/CodeSoul-co/THETA.

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