Innovation remains a critical driver of economic growth, organizational competitiveness, and societal progress, particularly within an increasingly data-driven digital environment. The exponential growth of unstructured textual data—originating from sources such as social media, customer feedback, academic literature, and patent databases—presents both a challenge and an opportunity for innovation management. Effectively extracting actionable insights from these diverse data streams has become essential for informed decision-making across the innovation lifecycle. This study explores the role of textual analytics, with a specific focus on topic modeling, as a robust methodological approach for uncovering latent knowledge embedded within large text corpora.

Topic modeling, a class of probabilistic and unsupervised machine learning techniques, enables the systematic identification of hidden thematic patterns in textual data. Although not a recent development, its relevance has significantly increased with advancements in natural language processing (NLP) and big data technologies. Positioned within the broader NLP framework, topic modeling provides a transparent, interpretable, and scalable solution for organizing and summarizing complex textual information. Unlike more recent artificial intelligence approaches such as large language models (LLMs), including BERT and GPT, which often face limitations related to generalization, computational cost, and potential inaccuracies, topic modeling offers a more controlled and domain-specific analytical framework.

This paper highlights both the theoretical foundations and practical applications of topic modeling in innovation contexts. It emphasizes how the technique can support strategic decision-making by revealing emerging trends, identifying knowledge gaps, and enhancing organizational learning processes. Furthermore, the study underscores the comparative advantages of topic modeling in terms of interpretability, reproducibility, and adaptability to specific research or business needs. By bridging theoretical insights with practical implementation, this work contributes to a deeper understanding of how textual analytics can be leveraged to drive innovation and sustain competitive advantage in the digital age.