@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 = {

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.

}, 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 = {

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 \< 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.

}, 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} } @article {861, title = {Automated Test Generation and Marking Using LLMs}, journal = {Electronics}, volume = {14}, year = {2025}, pages = {2835}, abstract = {

This paper presents an innovative exam-creation and grading system powered by advanced natural language processing and local large language models. The system automatically generates clear, grammatically accurate questions from both short passages and longer documents across different languages, supports multiple formats and difficulty levels, and ensures semantic diversity while minimizing redundancy, thus maximizing the percentage of the material that is covered in the generated exam paper. For grading, it employs a semantic-similarity model to evaluate essays and open-ended responses, awards partial credit, and mitigates bias from phrasing or syntax via named entity recognition. A major advantage of the proposed approach is its ability to run entirely on standard personal computers, without specialized artificial intelligence hardware, promoting privacy and exam security while maintaining low operational and maintenance costs. Moreover, its modular architecture allows the seamless swapping of models with minimal intervention, ensuring adaptability and the easy integration of future improvements. A requirements{\textendash}compliance evaluation, combined with established performance metrics, was used to review and compare two popular multilingual LLMs and monolingual alternatives, demonstrating the system{\textquoteright}s effectiveness and flexibility. The experimental results show that the system achieves a grading accuracy within a 17\% normalized error margin compared to that of human experts, with generated questions reaching up to 89.5\% semantic similarity to source content. The full exam generation and grading pipeline runs efficiently on consumer-grade hardware, with average inference times under 30 s.

}, keywords = {artificial intelligence, DeepSeek, exams, large language models, Llama, named entity recognition, Natural language processing, question{\textendash}answer generation, retrieval-augmented generation, Semantic Similarity, test generation, test marking}, issn = {2079-9292}, doi = {https://doi.org/10.3390/electronics14142835}, author = {Papachristou, Ioannis and Dimitroulakos, Grigoris and Vassilakis, Costas} } @article {857, title = {Stitching History into Semantics: LLM-Supported Knowledge Graph Engineering for 19th-Century Greek Bookbinding}, journal = {Machine Learning and Knowledge Extraction}, volume = {7}, year = {2025}, pages = {59}, abstract = {

Preserving cultural heritage can be efficiently supported by structured and semantic representation of historical artifacts. Bookbinding, a critical aspect of book history, provides valuable insights into past craftsmanship, material use, and conservation practices. However, existing bibliographic records often lack the depth needed to analyze bookbinding techniques, provenance, and preservation status. This paper presents a proof-of-concept system that explores how Large Language Models (LLMs) can support knowledge graph engineering within the context of 19th-century Greek bookbinding (1830{\textendash}1900), and as a result, generate a domain-specific ontology and a knowledge graph. Our ontology encapsulates materials, binding techniques, artistic styles, and conservation history, integrating metadata standards like MARC and Dublin Core to ensure interoperability with existing library and archival systems. To validate its effectiveness, we construct a Neo4j knowledge graph, based on the generated ontology and utilize Cypher Queries{\textemdash}including LLM-generated queries{\textemdash}to extract insights about bookbinding practices and trends. This study also explores how semantic reasoning over the knowledge graph can identify historical binding patterns, assess book conservation needs, and infer relationships between bookbinding workshops. Unlike previous bibliographic ontologies, our approach provides a comprehensive, semantically rich representation of bookbinding history, methods and techniques, supporting scholars, conservators, and cultural heritage institutions. By demonstrating how LLMs can assist in ontology/KG creation and query generation, we introduce and evaluate a semi-automated pipeline as a methodological demonstration for studying historical bookbinding, contributing to digital humanities, book conservation, and cultural informatics. Finally, the proposed approach can be used in other domains, thus, being generally applicable in knowledge engineering.

}, keywords = {bookbinding history, Cultural heritage, knowledge graphs, large language models, ontology engineering}, issn = {2504-4990}, doi = {https://doi.org/10.3390/make7030059}, author = {Doumanas, Dimitrios and Ntalouka, Efthalia and Vassilakis, Costas and Wallace, Manolis and Kotis, Konstantinos} }