@article {879,
	title = {Large Language Models as Explainable AI Ensemble Aggregators for Business Review Sentiment Analysis: A Comparative Study with Classical Ensembles},
	journal = {Applied Sciences},
	volume = {16},
	year = {2026},
	pages = {6479},
	abstract = {<p>Online business reviews encode rich customer sentiment that is critical for commercial decision making, yet accurately predicting star ratings from free text remains a challenging five-class classification problem. Classical ensemble methods{\textemdash}Soft Voting, Weighted Voting, and Stacking{\textemdash}aggregate complementary base-model outputs to improve predictive performance, but they produce opaque decisions that are unintelligible to business stakeholders. This paper proposes using a large language model (LLM), specifically unsloth/LLaMA-3.3-70B-Instruct, as an Explainable AI (XAI) ensemble aggregator: the LLM receives the predictions and confidence scores of four heterogeneous base models (Logistic Regression, Support Vector Machine, Na{\"\i}ve Bayes, and BERT-base-uncased) and reasons over them to produce both a final star-rating prediction and a natural-language explanation. We evaluate the full pipeline on 10,000-sample balanced and natural-distribution test sets derived from the Yelp Academic Dataset, with additional cross-lingual validation on Spanish Amazon Reviews. The LLM aggregator (LLAMA_AGG) achieves the highest macro-F1 on both pipelines (0.6800 on balanced; 0.6720 on natural) and the best ordinal calibration (QWK = 0.9111 on balanced; 0.9337 on natural), outperforming all classical aggregators and base models. A detailed Explainable AI analysis reveals that the LLM revises 28.07\% of its standalone predictions after observing the ensemble outputs, improving the accuracy by +22.2 percentage points on the revised cases. The aggregator corrects severe polar bias in the standalone LLM ({\textpm}0.35 recall improvement on mid-range star classes) and produces longer explanations when evidence is conflicted{\textemdash}a quantitative signal of deliberative reasoning. A formal human evaluation with two judges confirms high explanation faithfulness (4.47/5) and readability (4.82/5). Model scale ablation shows an 8B parameter variant achieves 90.8\% agreement with the 70B model, enabling practical deployment. These findings demonstrate that Explainable AI can be achieved through LLM-based ensemble aggregation, establishing a principled approach for business-review sentiment analysis.</p>
},
	keywords = {BERT, business reviews, chain-of-thought, ensemble learning, Explainable AI, large language models, Llama, meta-reasoning, Natural language processing, Sentiment Analysis, Soft Voting, Stacking, star rating prediction, Yelp},
	issn = {2076-3417},
	doi = {https://doi.org/10.3390/app16136479},
	url = {https://www.mdpi.com/2076-3417/16/13/6479},
	author = {Roumeliotis, Konstantinos I. and Margaris, Dionisis and Spiliotopoulos, Dimitris and Vassilakis, Costas}
}
