# Evaluating LLM Usage for Efficient and Explainable Numerical and Classified Implicit Sentiment Analysis of Product Desirability

arXiv: https://arxiv.org/abs/2606.23701 · HTML: https://mlsanity.com/paper/2606.23701/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 87 references that are in the map.
- First posted on arXiv 4 Jun 2026.

## Builds on (most cited first, 19 of 19)

- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop](https://mlsanity.com/paper/1506.03365/) (2015)
- [Energy and Policy Considerations for Deep Learning in NLP](https://mlsanity.com/paper/1906.02243/) (2019)
- [Deep Learning for Sentiment Analysis : A Survey](https://mlsanity.com/paper/1801.07883/) (2018)
- [Is ChatGPT a Good Sentiment Analyzer? A Preliminary Study](https://mlsanity.com/paper/2304.04339/) (2023)
- [Reasoning Implicit Sentiment with Chain-of-Thought Prompting](https://mlsanity.com/paper/2305.11255/) (2023)
- [TweetNLP: Cutting-Edge Natural Language Processing for Social Media](https://mlsanity.com/paper/2206.14774/) (2022)
- [EmoLLMs: A Series of Emotional Large Language Models and Annotation Tools for Comprehensive Affective Analysis](https://mlsanity.com/paper/2401.08508/) (2024)
- [If in a Crowdsourced Data Annotation Pipeline, a GPT-4](https://mlsanity.com/paper/2402.16795/) (2024)
- [Sentiment Analysis Based on Deep Learning: A Comparative Study](https://mlsanity.com/paper/2006.03541/) (2020)
- [Sentiment Analysis of Twitter Data for Predicting Stock Market Movements](https://mlsanity.com/paper/1610.09225/) (2016)
- [What do LLMs Know about Financial Markets? A Case Study on Reddit Market Sentiment Analysis](https://mlsanity.com/paper/2212.11311/) (2022)
- [Applying BERT and ChatGPT for Sentiment Analysis of Lyme Disease in Scientific Literature](https://mlsanity.com/paper/2302.06474/) (2023)
- [Don't Get Too Excited -- Eliciting Emotions in LLMs](https://mlsanity.com/paper/2503.02457/) (2025)
- [Using LLMs to Establish Implicit User Sentiment of Software Desirability](https://mlsanity.com/paper/2408.01527/) (2024)
- [Utilizing Large Language Models to Synthesize Product Desirability Datasets](https://mlsanity.com/paper/2411.13485/) (2024)
- [Scalable and Ethical Insider Threat Detection through Data Synthesis and Analysis by LLMs](https://mlsanity.com/paper/2502.07045/) (2025)
- [Causal Prompting for Implicit Sentiment Analysis with Large Language Models](https://mlsanity.com/paper/2507.00389/) (2025)
- [SherLOCKED: A Detective-themed Serious Game for Cyber Security Education](https://mlsanity.com/paper/2107.04506/) (2021)

## Abstract (arXiv)

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 review scores. Across the datasets, LLMs generated numerical sentiment scores directly from qualitative responses and closely matched expert labels, achieving Pearson correlations up to 0.97 and classification accuracy up to 94%. LLMs maintained robustness even when handling data presented in multiple forms and consistently expressed high confidence. In contrast, lexicon-based and transformer baselines did not produce statistically significant results. Among the models tested, GPT-4o-mini achieved performance comparable to larger models at 94% lower cost, supporting scalable deployment. The framework also incorporates model confidence ratings and human-readable rationale explanations (xAI), improving interpretability, transparency, and trust while supporting practical use in product satisfaction assessment. In general, using the PDT tool as a survey method along with a cost efficient LLM for sentiment analysis has the potential to provide for product evaluation with results that are rich in terms of sentiment scores (both numerical and classified sentiment) and in terms of the high-level user impressions of the product that can be used to identify ideas for product development and improvement, as well as marketing ideas for target audiences.