# Enhancing Sentiment Classification with Machine Learning and Combinatorial Fusion

arXiv: https://arxiv.org/abs/2510.27014 · HTML: https://mlsanity.com/paper/2510.27014/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 30 references that are in the map.
- First posted on arXiv 30 Oct 2025.

## Builds on (most cited first, 8 of 8)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [Energy and Policy Considerations for Deep Learning in NLP](https://mlsanity.com/paper/1906.02243/) (2019)
- [A Primer in BERTology: What we know about how BERT works](https://mlsanity.com/paper/2002.12327/) (2020)
- [LlamBERT: Large-scale low-cost data annotation in NLP](https://mlsanity.com/paper/2403.15938/) (2024)
- [Generating Effective Ensembles for Sentiment Analysis](https://mlsanity.com/paper/2402.16700/) (2024)

## Built on by (most cited first, 2 of 2)

- [Contextual Value Alignment via Multilayer Combinatorial Fusion](https://mlsanity.com/paper/2608.07642/) (2026)
- [Diversity-Aware Ensemble of Quantum-Classical Models via Combinatorial Fusion](https://mlsanity.com/paper/2610.13808/) (2026)

## Abstract (arXiv)

This paper presents a novel approach to sentiment classification using the application of Combinatorial Fusion Analysis (CFA) to integrate an ensemble of diverse machine learning models, achieving state-of-the-art accuracy on the IMDB sentiment analysis dataset of 97.072%. CFA leverages the concept of cognitive diversity, which utilizes rank-score characteristic functions to quantify the dissimilarity between models and strategically combine their predictions. This is in contrast to the common process of scaling the size of individual models, and thus is comparatively efficient in computing resource use. Experimental results also indicate that CFA outperforms traditional ensemble methods by effectively computing and employing model diversity. The approach in this paper implements the combination of a transformer-based model of the RoBERTa architecture with traditional machine learning models, including Random Forest, SVM, and XGBoost.