Evaluating LLM Usage for Efficient and Explainable Numerical and Classified Implicit Sentiment Analysis of Product Desirability
arXiv:2606.23701v1 Announce Type: cross Abstract: Qualitative product feedback can reveal nuanced user experiences, but its implicit sentiment is difficult to measure. This paper presents a scalable and interpretable framework that uses large language models (LLMs) to quantify product desirability from such data. Using two Product Desirability Toolkit (PDT) datasets from ZORQ and CARMA comprising 106 respondent term groupings with gold-standard human annotation, zero-shot continuous numerical sentiment scoring and categorical sentiment classification are evaluated without relying on explicit r
The proliferation of advanced large language models has enabled new approaches to complex qualitative data analysis, making this evaluation timely.
This development allows for more efficient and explainable quantification of implicit sentiment from qualitative feedback, crucial for product development and market understanding.
Businesses can now leverage LLMs for deeper, more scalable insights into customer desirability and feedback, potentially accelerating product cycles and market responsiveness.
- · AI software providers
- · Consumer product companies
- · Market research firms
- · Customer experience platforms
- · Traditional manual sentiment analysis services
Increased adoption of LLM-powered tools for qualitative data analysis in enterprise settings.
Improved product-market fit and accelerated innovation cycles for companies effectively using these insights.
Deeper, more nuanced understanding of consumer psychology influencing broader marketing and strategic decisions.
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Read at arXiv cs.AI