InsightNet: Structured Insight Mining from Customer Feedback
This addresses the problem of unstructured customer feedback analysis for businesses, though it appears incremental as it builds on existing methods with specific improvements.
The authors tackled the problem of automatically extracting structured insights from customer reviews by developing InsightNet, an end-to-end machine learning framework that builds a semi-supervised multi-level taxonomy and uses a multi-task architecture with LLM fine-tuning. The result was an 11% improvement in F1-score over state-of-the-art methods, achieving 0.85 in multi-label topic classification.
We propose InsightNet, a novel approach for the automated extraction of structured insights from customer reviews. Our end-to-end machine learning framework is designed to overcome the limitations of current solutions, including the absence of structure for identified topics, non-standard aspect names, and lack of abundant training data. The proposed solution builds a semi-supervised multi-level taxonomy from raw reviews, a semantic similarity heuristic approach to generate labelled data and employs a multi-task insight extraction architecture by fine-tuning an LLM. InsightNet identifies granular actionable topics with customer sentiments and verbatim for each topic. Evaluations on real-world customer review data show that InsightNet performs better than existing solutions in terms of structure, hierarchy and completeness. We empirically demonstrate that InsightNet outperforms the current state-of-the-art methods in multi-label topic classification, achieving an F1 score of 0.85, which is an improvement of 11% F1-score over the previous best results. Additionally, InsightNet generalises well for unseen aspects and suggests new topics to be added to the taxonomy.