SPSO-EFVM: A Particle Swarm Optimization- Based Ensemble Fusion Voting Model for Sentence-Level Sentiment Analysis

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dc.contributor.authorDimple Tiwari
dc.contributor.authorBharti Nagpal
dc.contributor.authorBhoopesh Singh Bhati
dc.contributor.authorManoj Gupta
dc.contributor.authorPannee Suanpang
dc.contributor.authorSujin Butdisuwan
dc.contributor.authorAziz Nanthaamornphong
dc.contributor.correspondenceM. Gupta; Guru Ghasidas Vishwavidyalaya Bilaspur, Department of Electrical Engineering, Bilaspur, Koni, Chhattisgarh, 495009, India; email: manojgupta35@yahoo.co.in; A. Nanthaamornphong; Prince of Songkla University, College of Computing, Phuket, 83120, Thailand; email: aziz.n@phuket.psu.ac.th
dc.date.accessioned2025-03-10T07:34:20Z
dc.date.available2025-03-10T07:34:20Z
dc.date.issued2024
dc.description.abstractSentiment analysis has received incremental growth in recent years for emerging applications, including human-robot integration, social platforms monitoring, and decision-support systems. Several neural or transformer model-based solutions have been provided in the field of sentiment analysis that relies on the decision of a single classifier or neural model. These are erroneous to encode contextual information into appropriate dialogues and increase extra computational cost and time. Hence, we proposed a compact and parameter-effective Particle Swarm Optimization-based Ensemble Fusion Voting Model (PSO-EFVM) that exploited the combined properties of four ensemble techniques, namely Adaptive-Boost, Gradient-Boost, Random-Forest, and Extremely-Randomized Tree with Particle Swarm Optimization (PSO)-based hyperparameter selection. The proposed model is investigated on five cross-domain datasets after applying the foremost initialization and feature extraction using Information Gain (IG). It employs adaptive and gradient learning to incorporate the automatic attribute selection with the arbitrary loss function optimization. In short, a generalized two-block composite classifier is designed to perform context compositionality and sentiment classification. A population-based meta-heuristic optimization PSO is applied to each base ensemble learner that calculates weights for the best parameter selection. Comprehensive investigations of different domains reveal the superiority of the proposed PSO-EFVM over established baselines and the latest state-of-the-art models. © 2013 IEEE.
dc.identifier.citationIEEE Access
dc.identifier.doi10.1109/ACCESS.2024.3363158
dc.identifier.issn21693536
dc.identifier.scopus2-s2.0-85184804855
dc.identifier.urihttps://repository.dusit.ac.th//handle/123456789/4466
dc.languageEnglish
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.rightsAll Open Access; Gold Open Access
dc.rights.holderScopus
dc.subjectadaptive-boosting
dc.subjectensemble learning
dc.subjectgradient-boosting
dc.subjectNatural language processing
dc.subjectparticle swarm optimization
dc.subjectsentiment analysis
dc.titleSPSO-EFVM: A Particle Swarm Optimization- Based Ensemble Fusion Voting Model for Sentence-Level Sentiment Analysis
dc.typeArticle
mods.location.urlhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85184804855&doi=10.1109%2fACCESS.2024.3363158&partnerID=40&md5=3dc60794b7c70670c8b5d5cd4ee9f4ce
oaire.citation.endPage23724
oaire.citation.startPage23707
oaire.citation.volume12
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