@conference {875,
	title = {A Comparative Study of Qwen and Gemma Large Language Models for Social Media Slang and Emoji Sentiment Analysis for Business Recommender Systems},
	booktitle = {Proceedings of the 2026 International Conference on Information, Intelligence, Systems and Applications},
	year = {2026},
	publisher = {IEEE Xplore},
	organization = {IEEE Xplore},
	abstract = {<p>Sentiment analysis of social media text remains a fundamentally challenging task due to the pervasive use of informal slang, abbreviations, deliberate spelling modifications, and emojis. Traditional lexicons and early machine learning models frequently struggle to decode the nuanced semantics of these modern digital expressions, particularly when distinguishing between genuine sentiment and sarcasm. In this paper, we present a comprehensive comparative study investigating the capabilities of two recent 4-billion-parameter large language models (LLMs), Qwen3.5 (4B) and Gemma3 (4B), in robust social media sentiment analysis. The core contribution of our study is the fine-tuning of these models at a comparable scale using 4-bit NormalFloat (NF4) quantization combined with the Unsloth library, establishing a highly efficient and reproducible training pipeline. We critically evaluate both models in zero-shot and fine-tuned settings on a diverse, emoji-heavy dataset characterized by four complex sentiment classes: Positive, Negative, Neutral, and Sarcastic. Our experimental results demonstrate that while both models show highly capable zero-shot generalization (Qwen achieving 75.95\% accuracy and Gemma achieving 73.20\%), they exhibit distinct failure modes surrounding sarcastic interpretations. However, alignment via Low-Rank Adaptation (LoRA) dramatically elevates their performance, allowing both to achieve 100\% accuracy on the held-out test set while maintaining distinctly competitive inference speeds suitable for real-time applications. We also provide an exhaustive architectural analysis of the models{\textquoteright} inference latency, per-class prediction bottlenecks, and training efficiency, highlighting practical considerations and trade-offs for deploying lightweight LLMs in real-world sentiment analysis environments. The strong inference-speed profile of both fine-tuned models makes them directly deployable as opinion-mining modules within business recommender system pipelines, where timely and accurate decoding of customer-expressed sentiment{\textemdash}including sarcasm{\textemdash}translates into richer preference signals and more reliable personalization.</p>
},
	keywords = {Gemma, large language models, LoRA, Quantization, Qwen, Sentiment Analysis, social media, Unsloth},
	author = {Konstantinos I. Roumeliotis and Dimitris Spiliotopoulos and Christos Tryfonopoulos and Dionisis Margaris and Giorgos Mpardis and Costas Vassilakis}
}
@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}
}
