محمدجواد محدث

In short product-lifecycle industries, the rapid identification of research an‎d development (R&D) priorities plays a vital role in ensuring the survival of manufacturing firms amid intense market competition. On the other han‎d, customer an‎d user opinions constitute one of the most important sources for identifying their needs an‎d, consequently, determining R&D priorities. Accurate an‎d effective R&D decisions are possible only when companies can quickly, timely, an‎d precisely utilize real user-experience data an‎d identify customer needs. The Voice of Customer (VoC) surrounding a product is one of the most valuable information sources for understan‎ding customer needs, expectations, an‎d product weaknesses. In this study, data mining techniques an‎d the aspect-based sentiment analysis (ABSA) model were employed to rapidly extract an‎d analyze customer opinions on four of the best-selling an‎d most popular laptop models from the world’s largest online retailer (Amazon.com). The goal was to identify R&D priorities by quickly detecting strengths, weaknesses, an‎d user-driven needs. To extract aspects an‎d determine sentiment polarity, the multilingual PyABSA model was used, after which the results were analyzed through charts an‎d heat maps. The findings indicated that display quality, product design an‎d materials, an‎d battery quality an‎d longevity had the greatest impact on customer satisfaction. Conversely, issues related to the operating system an‎d auxiliary applications, along with dissatisfaction regarding keyboard an‎d touchpad quality, accounted for the highest levels of negative sentiment. Furthermore, the results suggest that manufacturers operating in short product-lifecycle environments should, while maintaining the highly satisfying aspects as the core of their competitive advantage, prioritize aspects associated with the highest dissatisfaction by conducting secondary analyses of development cost an‎d opportunity for each aspect. Integrating large-scale customer data analysis with ABSA methods can offer an effective, low-cost, an‎d evidence-based approach to guiding product development decisions in similar industries.

تحقيق و توسعه , صداي مشتري , تحليل احساسات مبتني بر جنبه , داده‌كاوي , چرخه عمر كوتاه

research an‎d development , Voice of Customer , Aspect-based sentiment analysis , Data Mining , Short Product Lifecycle

Mohamadjavad Mohades

Dr. Bonyadi