A Method for Extracting Dissatisfaction Entities and Visualizing Dissatisfaction Information Using a Large Language Model

In today’s VUCA era, Companies confront rapidly shifting consumer demands and market landscapes, making new business development indispensable for both sustainable growth and long-term competitiveness. Despite its importance, the failure rate for new ventures remains high, largely stemming from the persistent challenge of accurately discerning market needs and understanding customer dissatisfaction. To address this gap, our study presents a strategy that synergizes large language models (LLMs) with prompt engineering to (1) extract “dissatisfaction entities,” (2) automatically compile a “dissatisfaction dictionary,” and (3) visualize the interrelationships among these detected dissatisfaction factors. Specifically, we employ the Dissatisfaction Survey Dataset furnished by the National Institute of Informatics, narrowing our focus to the “Outdoor Sports_Motorcycles” category. Utilizing a Conversation Chain built atop the OpenAI GPT-4o API and the LangChain library, we systematically process the dataset’s user posts to extract 306 unique dissatisfaction entities. These entities are then distilled into a structured dictionary, capturing the essence of each complaint. Next, we apply TF–IDF and cosine similarity to visualize how these entities relate to one another, thereby exposing clusters or themes of user frustration. Through this analysis, noise and safety concerns emerge as primary, interlinked themes: nighttime engine noise, particularly in urban areas, frequently correlates with perceptions of reckless riding or increased accident risk. These finding highlights both product-design opportunities—such as improved mufflers or noise-suppression technologies—and strategic considerations for municipal policies and traffic regulations. More broadly, our method underscores the power of a systematic, data-driven approach to capturing and organizing customer dissatisfaction, which can serve as a linchpin for minimizing uncertainty in planning and accelerating effective decision-making. By pinpointing precisely where and how customers are dissatisfied, businesses can tailor product enhancements, allocate resources more strategically, and advance new business ventures with heightened clarity.