Enhancing Business Intelligence through Unsolicited Feedback Analysis (2025 - 2026)
The primary technology challenge this project will address is the extraction and analysis of customer opinions from unsolicited feedback on platforms. While there has been significant research in aspect-based sentiment analysis, existing methods face several critical challenges including lack of standardization and reproducibility, difficulty in detecting latent aspects, challenges in handling unsolicited reviews, data augmentation limitations, and industry-specific fine-tuning and labeling. By addressing these challenges, this project aims to develop a robust, scalable solution that leverages advanced Natural Language Processing (NLP) and machine learning techniques to automatically identify, extract, and analyze both explicit and latent aspects and corresponding opinions from large volumes of reviews. This innovative approach will fill the existing gaps in aspect-based sentiment analysis, improve the reliability of insights derived from customer feedback, and enhance the overall feedback management process.
Customer Feedback Analytics from Unsolicited Resources (2022 - 2026)
Through systematic monitoring and analysis of customers’ feedback (reviews), Customer Feedback Management (CFM) systems have been extensively developed in different industries to improve customer satisfaction and experience. Existing systems, however, have mainly relied on solicited feedback channels, such as surveys, and have not considered leveraging online social platforms where customers can leave unsolicited feedback publicly (social feedback) without the constraints of private customer-industry feedback channels. Press’nXPress (PXP) is an IoT-based B2B software company specializing in CFM systems and seeks to be among the first to leverage these unsolicited public feedback outlets. The main goal of this research partnership is to assist PXP to analyze the unsolicited feedback, deliver business intelligence, and automate meaningful insights from customer conversations by uniquely applying the University of Windsor’s expertise in Social Network Analysis (SNA) and Natural Language Processing (NLP) techniques to PXP’s existing service offerings, while acting as a foundation for the development of a long-standing relationship.