SPSO-EFVM: A Particle Swarm OptimizationBased Ensemble Fusion Voting Model for Sentence-Level Sentiment Analysis
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Date
2024-06-13
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IEEE ACCESS
Journal Title
SPSO-EFVM: A Particle Swarm OptimizationBased Ensemble Fusion Voting Model for Sentence-Level Sentiment Analysis
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Abstract
T Sentiment 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, GradientBoost, 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.
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DIMPLE TIWARI 1,2, BHARTI NAGPAL1 , BHOOPESH SINGH BHATI 3 , MANOJ GUPTA 4 , (Member, IEEE), PANNEE SUANPANG 5 , (Member, IEEE), SUJIN BUTDISUWAN 6 , AND AZIZ NANTHAAMORNPHONG 7 , (Member, IEEE)