Shyam Mohan
2023
InsightNet : Structured Insight Mining from Customer Feedback
Sandeep Sricharan Mukku
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Manan Soni
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Chetan Aggarwal
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Jitenkumar Rana
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Promod Yenigalla
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Rashmi Patange
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Shyam Mohan
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track
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.
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Co-authors
- Sandeep Sricharan Mukku 1
- Manan Soni 1
- Chetan Aggarwal 1
- Jitenkumar Rana 1
- Promod Yenigalla 1
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