Enhancing Sentiment Classification with Machine Learning and Combinatorial Fusion Article Swipe

PDF

Scott B. Patten , Pin‐Yu Chen , Christina Schweikert , Daniel Hsu ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2510.27014

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.

Related Topics

Iq Classification

Köppen Climate Classification

Classification Of Demons

Dewey Decimal Classification

Stellar Classification

Car Classification

Classification Of Swords

Anti-Russian Sentiment

Anti-Christian Sentiment

Anti-Western Sentiment

Globally Harmonized System Of Classification And Labelling Of Chemicals

Anti-German Sentiment

Concepts

No concepts available.

Metadata

Type: preprint
Landing Page: http://arxiv.org/abs/2510.27014
PDF: https://arxiv.org/pdf/2510.27014
OA Status: green
OpenAlex ID: https://openalex.org/W4415914367

All OpenAlex metadata

Raw OpenAlex JSON

OpenAlex ID: https://openalex.org/W4415914367

Canonical identifier for this work in OpenAlex
DOI: https://doi.org/10.48550/arxiv.2510.27014

Digital Object Identifier
Title: Enhancing Sentiment Classification with Machine Learning and Combinatorial Fusion

Work title
Type: preprint

OpenAlex work type
Publication year: 2025

Year of publication
Publication date: 2025-10-30

Full publication date if available
Authors: Scott B. Patten, Pin‐Yu Chen, Christina Schweikert, Daniel Hsu

List of authors in order
Landing page: https://arxiv.org/abs/2510.27014

Publisher landing page
PDF URL: https://arxiv.org/pdf/2510.27014

Direct link to full text PDF
Open access: Yes

Whether a free full text is available
OA status: green

Open access status per OpenAlex
OA URL: https://arxiv.org/pdf/2510.27014

Direct OA link when available
Cited by: 0

Total citation count in OpenAlex

Full payload

id	https://openalex.org/W4415914367
doi	https://doi.org/10.48550/arxiv.2510.27014
ids.doi	https://doi.org/10.48550/arxiv.2510.27014
ids.openalex	https://openalex.org/W4415914367
fwci
type	preprint
title	Enhancing Sentiment Classification with Machine Learning and Combinatorial Fusion
biblio.issue
biblio.volume
biblio.last_page
biblio.first_page
is_xpac	False
apc_list
apc_paid
language
locations[0].id	pmh:oai:arXiv.org:2510.27014
locations[0].is_oa	True
locations[0].source.id	https://openalex.org/S4306400194
locations[0].source.issn
locations[0].source.type	repository
locations[0].source.is_oa	True
locations[0].source.issn_l
locations[0].source.is_core	False
locations[0].source.is_in_doaj	False
locations[0].source.display_name	arXiv (Cornell University)
locations[0].source.host_organization	https://openalex.org/I205783295
locations[0].source.host_organization_name	Cornell University
locations[0].source.host_organization_lineage	https://openalex.org/I205783295
locations[0].license
locations[0].pdf_url	https://arxiv.org/pdf/2510.27014
locations[0].version	submittedVersion
locations[0].raw_type	text
locations[0].license_id
locations[0].is_accepted	False
locations[0].is_published	False
locations[0].raw_source_name
locations[0].landing_page_url	http://arxiv.org/abs/2510.27014
locations[1].id	doi:10.48550/arxiv.2510.27014
locations[1].is_oa	True
locations[1].source.id	https://openalex.org/S4306400194
locations[1].source.issn
locations[1].source.type	repository
locations[1].source.is_oa	True
locations[1].source.issn_l
locations[1].source.is_core	False
locations[1].source.is_in_doaj	False
locations[1].source.display_name	arXiv (Cornell University)
locations[1].source.host_organization	https://openalex.org/I205783295
locations[1].source.host_organization_name	Cornell University
locations[1].source.host_organization_lineage	https://openalex.org/I205783295
locations[1].license	cc-by
locations[1].pdf_url
locations[1].version
locations[1].raw_type	article
locations[1].license_id	https://openalex.org/licenses/cc-by
locations[1].is_accepted	False
locations[1].is_published
locations[1].raw_source_name
locations[1].landing_page_url	https://doi.org/10.48550/arxiv.2510.27014
indexed_in	arxiv, datacite
authorships[0].author.id	https://openalex.org/A5012744052
authorships[0].author.orcid	https://orcid.org/0000-0001-9871-4041
authorships[0].author.display_name	Scott B. Patten
authorships[0].author_position	first
authorships[0].raw_author_name	Patten, Sean
authorships[0].is_corresponding	False
authorships[1].author.id	https://openalex.org/A5050344371
authorships[1].author.orcid	https://orcid.org/0000-0003-1039-8369
authorships[1].author.display_name	Pin‐Yu Chen
authorships[1].author_position	middle
authorships[1].raw_author_name	Chen, Pin-Yu
authorships[1].is_corresponding	False
authorships[2].author.id	https://openalex.org/A5036063517
authorships[2].author.orcid
authorships[2].author.display_name	Christina Schweikert
authorships[2].author_position	middle
authorships[2].raw_author_name	Schweikert, Christina
authorships[2].is_corresponding	False
authorships[3].author.id	https://openalex.org/A5067069179
authorships[3].author.orcid
authorships[3].author.display_name	Daniel Hsu
authorships[3].author_position	last
authorships[3].raw_author_name	Hsu, D. Frank
authorships[3].is_corresponding	False
has_content.pdf	False
has_content.grobid_xml	False
is_paratext	False
open_access.is_oa	True
open_access.oa_url	https://arxiv.org/pdf/2510.27014
open_access.oa_status	green
open_access.any_repository_has_fulltext	False
created_date	2025-11-05T00:00:00
display_name	Enhancing Sentiment Classification with Machine Learning and Combinatorial Fusion
has_fulltext	False
is_retracted	False
updated_date	2025-11-06T06:51:31.235846
primary_topic
cited_by_count	0
locations_count	2
best_oa_location.id	pmh:oai:arXiv.org:2510.27014
best_oa_location.is_oa	True
best_oa_location.source.id	https://openalex.org/S4306400194
best_oa_location.source.issn
best_oa_location.source.type	repository
best_oa_location.source.is_oa	True
best_oa_location.source.issn_l
best_oa_location.source.is_core	False
best_oa_location.source.is_in_doaj	False
best_oa_location.source.display_name	arXiv (Cornell University)
best_oa_location.source.host_organization	https://openalex.org/I205783295
best_oa_location.source.host_organization_name	Cornell University
best_oa_location.source.host_organization_lineage	https://openalex.org/I205783295
best_oa_location.license
best_oa_location.pdf_url	https://arxiv.org/pdf/2510.27014
best_oa_location.version	submittedVersion
best_oa_location.raw_type	text
best_oa_location.license_id
best_oa_location.is_accepted	False
best_oa_location.is_published	False
best_oa_location.raw_source_name
best_oa_location.landing_page_url	http://arxiv.org/abs/2510.27014
primary_location.id	pmh:oai:arXiv.org:2510.27014
primary_location.is_oa	True
primary_location.source.id	https://openalex.org/S4306400194
primary_location.source.issn
primary_location.source.type	repository
primary_location.source.is_oa	True
primary_location.source.issn_l
primary_location.source.is_core	False
primary_location.source.is_in_doaj	False
primary_location.source.display_name	arXiv (Cornell University)
primary_location.source.host_organization	https://openalex.org/I205783295
primary_location.source.host_organization_name	Cornell University
primary_location.source.host_organization_lineage	https://openalex.org/I205783295
primary_location.license
primary_location.pdf_url	https://arxiv.org/pdf/2510.27014
primary_location.version	submittedVersion
primary_location.raw_type	text
primary_location.license_id
primary_location.is_accepted	False
primary_location.is_published	False
primary_location.raw_source_name
primary_location.landing_page_url	http://arxiv.org/abs/2510.27014
publication_date	2025-10-30
publication_year	2025
referenced_works_count	0
abstract_inverted_index.a	3, 110
abstract_inverted_index.an	19
abstract_inverted_index.by	94
abstract_inverted_index.in	62, 80, 103
abstract_inverted_index.is	61, 77
abstract_inverted_index.of	12, 21, 35, 41, 68, 72, 109, 113
abstract_inverted_index.on	29
abstract_inverted_index.to	6, 17, 49, 64
abstract_inverted_index.CFA	37, 89
abstract_inverted_index.The	101
abstract_inverted_index.and	55, 75, 97, 126
abstract_inverted_index.the	10, 30, 39, 51, 65, 70, 107, 114
abstract_inverted_index.IMDB	31
abstract_inverted_index.SVM,	125
abstract_inverted_index.This	0, 60
abstract_inverted_index.also	86
abstract_inverted_index.size	71
abstract_inverted_index.that	88
abstract_inverted_index.this	104
abstract_inverted_index.thus	76
abstract_inverted_index.use.	83
abstract_inverted_index.with	117
abstract_inverted_index.(CFA)	16
abstract_inverted_index.model	99, 112
abstract_inverted_index.novel	4
abstract_inverted_index.paper	1, 105
abstract_inverted_index.their	58
abstract_inverted_index.using	9
abstract_inverted_index.which	44
abstract_inverted_index.Fusion	14
abstract_inverted_index.Random	123
abstract_inverted_index.common	66
abstract_inverted_index.models	54
abstract_inverted_index.Forest,	124
abstract_inverted_index.RoBERTa	115
abstract_inverted_index.between	53
abstract_inverted_index.combine	57
abstract_inverted_index.concept	40
abstract_inverted_index.dataset	34
abstract_inverted_index.diverse	22
abstract_inverted_index.machine	23, 119
abstract_inverted_index.methods	93
abstract_inverted_index.models,	25, 74, 121
abstract_inverted_index.process	67
abstract_inverted_index.results	85
abstract_inverted_index.scaling	69
abstract_inverted_index.Analysis	15
abstract_inverted_index.XGBoost.	127
abstract_inverted_index.accuracy	28
abstract_inverted_index.analysis	33
abstract_inverted_index.approach	5, 102
abstract_inverted_index.contrast	63
abstract_inverted_index.ensemble	20, 92
abstract_inverted_index.indicate	87
abstract_inverted_index.learning	24, 120
abstract_inverted_index.presents	2
abstract_inverted_index.quantify	50
abstract_inverted_index.resource	82
abstract_inverted_index.utilizes	45
abstract_inverted_index.97.072\%.	36
abstract_inverted_index.achieving	26
abstract_inverted_index.cognitive	42
abstract_inverted_index.computing	81, 96
abstract_inverted_index.efficient	79
abstract_inverted_index.employing	98
abstract_inverted_index.functions	48
abstract_inverted_index.including	122
abstract_inverted_index.integrate	18
abstract_inverted_index.leverages	38
abstract_inverted_index.sentiment	7, 32
abstract_inverted_index.diversity,	43
abstract_inverted_index.diversity.	100
abstract_inverted_index.implements	106
abstract_inverted_index.individual	73
abstract_inverted_index.rank-score	46
abstract_inverted_index.application	11
abstract_inverted_index.combination	108
abstract_inverted_index.effectively	95
abstract_inverted_index.outperforms	90
abstract_inverted_index.traditional	91, 118
abstract_inverted_index.Experimental	84
abstract_inverted_index.architecture	116
abstract_inverted_index.predictions.	59
abstract_inverted_index.Combinatorial	13
abstract_inverted_index.comparatively	78
abstract_inverted_index.dissimilarity	52
abstract_inverted_index.strategically	56
abstract_inverted_index.characteristic	47
abstract_inverted_index.classification	8
abstract_inverted_index.state-of-the-art	27
abstract_inverted_index.transformer-based	111
cited_by_percentile_year
countries_distinct_count	0
institutions_distinct_count	4
citation_normalized_percentile