Enhancing Sentiment Classification with Machine Learning and Combinatorial Fusion

Sean Patten, Pin-Yu Chen, Christina Schweikert, D. Frank Hsu
10/30/2025
cs.LG

No official code found. Best implementation: seanpattencode/CFA4SA (0 stars, last commit 2025-07-12).

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.

View on arXivView PDF

Code Implementations(1)

seanpattencode/CFA4SACommunity64%

This contains the code for the paper "Enhancing Sentiment Classification with Machine Learning and Combinatorial Fusion"

00PythonJul 12, 20251 years ago

Similar Papers

Performance evaluation of Reddit Comments using Machine Learning and Natural Language Processing methods in Sentiment Analysis

83% match2024

Sentiment analysis of texts from social networks based on machine learning methods for monitoring public sentiment

81% match2025

TWSSenti: A Novel Hybrid Framework for Topic-Wise Sentiment Analysis on Social Media Using Transformer Models

81% match2025

CPC-CMS: Cognitive Pairwise Comparison Classification Model Selection Framework for Document-level Sentiment Analysis

81% match2025

Multimodal Sentiment Analysis on CMU-MOSEI Dataset using Transformer-based Models

81% match2025

Lowering the Barrier of Machine Learning: Achieving Zero Manual Labeling in Review Classification Using LLMs

80% match2025

Cite this paper

@article{patten2025enhancing,
  title  = {Enhancing Sentiment Classification with Machine Learning and Combinatorial Fusion},
  author = {Sean Patten and Pin-Yu Chen and Christina Schweikert and D. Frank Hsu},
  year   = {2025},
  eprint = {2510.27014},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2510.27014v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2510.27014.svg)](https://codeofpaper.com/paper/2510.27014)

Discussion