Enhancing Sentiment Classification with Machine Learning and Combinatorial Fusion

TL;DR

This paper introduces Combinatorial Fusion Analysis (CFA), a novel ensemble method that combines diverse machine learning models for sentiment classification, achieving high accuracy efficiently by leveraging model dissimilarity.

Contribution

The paper proposes CFA, a new ensemble technique that uses cognitive diversity and rank-score functions to improve sentiment classification accuracy over traditional methods.

Findings

Achieved 97.072% accuracy on IMDB dataset.

Outperformed traditional ensemble methods.

Efficiently combines diverse models like RoBERTa, Random Forest, SVM, XGBoost.

Abstract

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.