Fine-Tuning Topics through Weighting Aspect Keywords
This addresses the challenge for organizations in deriving insights from specialized text data, though it is incremental as it enhances existing topic modeling with expert guidance rather than introducing a new paradigm.
The paper tackles the problem of conventional topic modeling being static and lacking contextual awareness for specialized text data like quantum cryptography, by developing a framework that weights aspects based on expert input to improve topic relevance and document alignment. The result showed increased intra-cluster similarity and better thematic accuracy for documents in quantum communication research, adapting to shifts from theoretical to implementation discussions in conference papers.
Organizations face growing challenges in deriving meaningful insights from vast amounts of specialized text data. Conventional topic modeling techniques are typically static and unsupervised, making them ill-suited for fast-evolving fields like quantum cryptography. These models lack contextual awareness and cannot easily incorporate emerging expert knowledge or subtle shifts in subdomains. Moreover, they often overlook rare but meaningful terms, limiting their ability to surface early signals or align with expert-driven insights essential for strategic understanding. To tackle these gaps, we employ design science research methodology to create a framework that enhances topic modeling by weighting aspects based on expert-informed input. It combines expert-curated keywords with topic distributions iteratively to improve topic relevance and document alignment accuracy in specialized research areas. The framework comprises four phases, including (1) initial topic modeling, (2) expert aspect definition, (3) supervised document alignment using cosine similarity, and (4) iterative refinement until convergence. Applied to quantum communication research, this method improved the visibility of critical but low-frequency terms. It also enhanced topic coherence and aligned topics with the cryptographic priorities identified by experts. Compared to the baseline model, this framework increased intra-cluster similarity. It reclassified a substantial portion of documents into more thematically accurate clusters. Evaluating QCrypt 2023 and 2024 conference papers showed that the model adapts well to changing discussions, marking a shift from theoretical foundations to implementation challenges. This study illustrates that expert-guided, aspect-weighted topic modeling boosts interpretability and adaptability.