@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}
}
@article {868,
	title = {A Large-Scale Empirical Study of LLM Orchestration and Ensemble Strategies for Sentiment Analysis in Recommender Systems},
	journal = {Future Internet},
	volume = {18},
	year = {2026},
	pages = {112},
	abstract = {<p>This paper presents a comprehensive empirical evaluation comparing meta-model aggregation strategies with traditional ensemble methods and standalone models for sentiment analysis in recommender systems beyond standalone large language model (LLM) performance. We investigate whether aggregating multiple LLMs through a reasoning-based meta-model provides measurable performance advantages over individual models and standard statistical aggregation approaches in zero-shot sentiment classification. Using a balanced dataset of 5000 verified Amazon purchase reviews (1000 reviews per rating category from 1 to 5 stars, sampled via two-stage stratified sampling across five product categories), we evaluate 12 different leading pre-trained LLMs from four major providers (OpenAI, Anthropic, Google, and DeepSeek) in both standalone and meta-model configurations. Our experimental design systematically compares individual model performance against GPT-based meta-model aggregation and traditional ensemble baselines (majority voting, mean aggregation). Results show statistically significant improvements (McNemar{\textquoteright}s test, p \&lt; 0.001): the GPT-5 meta-model achieves 71.40\% accuracy (10.15 percentage point improvement over the 61.25\% individual model average), while the GPT-5 mini meta-model reaches 70.32\% (9.07 percentage point improvement). These observed improvements surpass traditional ensemble methods (majority voting: 62.64\%; mean aggregation: 62.96\%), suggesting potential value in meta-model aggregation for sentiment analysis tasks. Our analysis reveals empirical patterns including neutral sentiment classification challenges (3-star ratings show 64.83\% failure rates across models), model influence hierarchies, and cost-accuracy trade-offs ($130.45 aggregation cost vs. $0.24{\textendash}$43.97 for individual models per 5000 predictions). This work provides evidence-based insights into the comparative effectiveness of LLM aggregation strategies in recommender systems, demonstrating that meta-model aggregation with natural language reasoning capabilities achieves measurable performance gains beyond statistical aggregation alone.</p>
},
	keywords = {empirical evaluation, ensemble learning, large language models, LLM orchestration, meta-model aggregation, multi-model aggregation, reasoning models, Recommender Systems, sentiment classification, zero-shot sentiment analysis},
	issn = {1999-5903},
	doi = {https://doi.org/10.3390/fi18020112},
	url = {https://www.mdpi.com/1999-5903/18/2/112},
	author = {Roumeliotis, Konstantinos I. and Margaris, Dionisis and Spiliotopoulos, Dimitris and Vassilakis, Costas}
}
