## 1 Introduction  
In recent years, there has been a convergence between the fields of requirements-engineering and semantic technologies [1]. The progress in semantic concepts and technologies opens up new possibilities for utilizing natural language processing, artificial intelligence, and language-transformer technologies to define information and knowledge semantics across various domains.  
The Internet has become a powerful platform for online shopping, combined with interpersonal communication, leading consumers to increasingly rely on online sources for information about products they are interested in purchasing. Online reviews have experienced explosive growth in recent years, with the most prominent E-commerce platforms hosting hundreds of millions of reviews. The abundance of product feedback conveyed through online reviews has presented firms with a compelling new source of information. When revising their products, designers often consider multiple data sources, such as focus groups, consumer complaints, warranty claims, and ingenuity. Experimental evidence suggests that incorporating consumer feedback into product design, instead of solely relyingon laboratory-based approaches, leads to improved sales performance [2]. Consequently, there is a growing call in academia and industry for methods that prioritize consumer-driven innovation, as addressing consumer preferences has enhanced satisfaction and competitive positioning.  
However, the sheer volume of online reviews makes it nearly impossible for practitioners to analyze them comprehensively and manually. Therefore, automated methods for analyzing the content of online reviews offer the potential for significant time savings and ensure that firms can process and respond to critical feedback efficiently. Integrating these methods into the requirements-elicitation process holds great promise for transforming large amounts of data into human-readable requirements, enabling a deeper understanding of consumer preferences, driving processes and product innovation.  
To address this gap, we propose a requirements-elicitation algorithm based on domain-specific natural language processing or language-transformer technologies. Within requirements elicitation, \"domain-specific\" embodies a focused and finely-tuned approach that harmonizes seamlessly with a specific domain's distinctive attributes, terminologies, and intricacies. This process entails the meticulous tailoring of our algorithmic framework to adjust effortlessly to the nuances and particulars of the Amazon Echo Dot domain. Our algorithm surpasses traditional known approaches in enhancing the quality of the process and product innovation derived from online reviews. [PERSON] et al. review the current state-of-the-art procedures for data-driven requirements elicitation from dynamic data sources. The findings indicate that existing automated requirements elicitation predominantly relies on human-sourced data, particularly online reviews, as requirements sources and employs supervised machine-learning techniques for data processing. However, these approaches often result in merely identifying and classifying requirements-related information or features without generating requirements in a readily usable form. Their article highlights the pressing need to develop methods to leverage process-mediated and machine-generated data in requirements elicitation [3].  
Overall, our paper aims to advance requirements engineering by exploring the potential of an algorithm via language-transformer-enhanced opportunity mining from online reviews, empowered by natural language processing, artificial intelligence, or language-transformer technologies, to bridge the gap between data and human-readable requirements.