InsightNet: Structured Insight Mining from Customer Feedback
<|reference_start|>InsightNet: Structured Insight Mining from Customer Feedback: 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.<|reference_end|>
@article{mukku2024insightnet:,
    title={InsightNet: Structured Insight Mining from Customer Feedback},
    author={Sandeep Sricharan Mukku, Manan Soni, Jitenkumar Rana, Chetan Aggarwal,
  Promod Yenigalla, Rashmi Patange and Shyam Mohan},
    journal={arXiv preprint arXiv:2405.07195},
    year={2024},
    doi={10.18653/v1/2023.emnlp-industry.53},
    archivePrefix={arXiv},
    eprint={2405.07195},
    primaryClass={cs.CL cs.AI}
}