Abstract
Organizations can face challenges in processing large volumes of diverse product feedback from disparate sources, such as in-product forms and social media platforms. Manual analysis can be slow and difficult to scale, while some text summarization tools may lack the product-specific context to generate actionable insights. This disclosure describes systems and methods for automated feedback analysis that can utilize a data processing pipeline to ingest and standardize user feedback. An analytical engine, which may employ machine learning models, can operate in conjunction with a product-specific contextual knowledge base to classify feedback, generate narrative summaries, and perform sentiment analysis. This approach can transform unstructured user commentary into structured, context-aware insights, providing quantifiable metrics, trend analysis, and near real-time monitoring to support data-informed product development and performance measurement.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Fitzpatrick, Michael R.; Singhal, Kanupriya; Ganem, Steve; and Yao, Jian, "Automated Synthesis of Disparate Product Feedback Using a Contextual Knowledge Base", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/9734