Linking Customer Sentiment to Patent-Based Solutions Through Semantic Analysis

Abstract

Featured Application A practical application of the proposed approach is demonstrated in the Heating, Ventilation, and Air Conditioning (HVAC) field through the analysis of customer feedback and patent data. In this context, problems expressed by customers are semantically linked to relevant technological solutions, and through these matches, potential product features that can provide input to the product development process are revealed. Beyond this specific example, the approach can be applied to other technology-intensive sectors where large volumes of unstructured customer feedback and patent documents are available, for the purpose of systematic problem-solving and product feature identification.Abstract Analyzing customer feedback is critical for identifying unmet needs in product development and innovation processes. However, current studies often focus only on identifying customer-expressed problems, neglecting to systematically match these problems with technological solutions and transform them into potential product features. This study aims to propose a sentiment and semantic analysis-based approach that correlates problems derived from customer feedback with patent-based solutions. The proposed approach utilizes Aspect-Based Sentiment Analysis to identify unmet needs from customer feedback, the BERTopic algorithm to extract solution-oriented themes from patent documents, and short text semantic similarity methods to associate problem-solution pairs. The applicability of the approach is demonstrated using 476 customer product reviews and 3548 patents in the Heating, Ventilation, and Air Conditioning (HVAC) field. The results show that customer-expressed problems can be semantically correlated with patent-based technological solutions, and these matches contribute to the identification of potential product features. The resulting problem-solution matches are structured along technological development horizons and presented as a technology roadmap output. The proposed approach offers a framework supporting systematic problem-solution matching based on sentiment and semantic analysis in technology-intensive sectors with large volumes of unstructured text data.