Multi-level particle swarm optimisation and its parallel version for parameter optimisation of ensemble models: a case of sentiment polarity prediction

Budhi, Gregorius Satia; Chiong, Raymond; Dhakal, Sandeep

doi:10.1007/s10586-020-03093-3

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2023

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Cited by 2 publications

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“…Adamu et al [ 36 ] proposed a model using the particle swarm optimization technique for feature selection and evaluated the model's performance. Budh et al [ 37 ] proposed a model using multi-Level PSO (ML-PSO) to optimize the parameters of ensemble models for customer sentiment analysis—the proposed model improved prediction accuracy. Zare et al [ 38 ] proposed a model using the extracting knowledge for customer data using an improved k-means algorithm.…”

Section: Literature Reviewmentioning

confidence: 99%

A blockchain-based evaluation approach to analyse customer satisfaction using AI techniques

Barik

Misra

Ray³

et al. 2023

Heliyon

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Section: Literature Reviewmentioning

confidence: 99%

A blockchain-based evaluation approach to analyse customer satisfaction using AI techniques

Barik

Misra

Ray³

et al. 2023

Heliyon

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A Multi-type Classifier Ensemble for Detecting Fake Reviews Through Textual-based Feature Extraction

Budhi

Chiong

2023

ACM Trans. Internet Technol.

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The financial impact of online reviews has prompted some fraudulent sellers to generate fake consumer reviews for either promoting their products or discrediting competing products. In this study, we propose a novel ensemble model—the Multi-type Classifier Ensemble (MtCE)—combined with a textual-based featuring method, which is relatively independent of the system, to detect fake online consumer reviews. Unlike other ensemble models that utilise only the same type of single classifier, our proposed ensemble utilises several customised machine learning classifiers (including deep learning models) as its base classifiers. The results of our experiments show that the MtCE can adequately detect fake reviews, and that it outperforms other single and ensemble methods in terms of accuracy and other measurements in all the relevant public datasets used in this study. Moreover, if set correctly, the parameters of MtCE, such as base-classifier types, the total number of base classifiers, bootstrap and the method to vote on output (e.g., majority or priority), further improve the performance of the proposed ensemble.

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Multi-level particle swarm optimisation and its parallel version for parameter optimisation of ensemble models: a case of sentiment polarity prediction

Cited by 2 publications

References 60 publications

A blockchain-based evaluation approach to analyse customer satisfaction using AI techniques

A blockchain-based evaluation approach to analyse customer satisfaction using AI techniques

A Multi-type Classifier Ensemble for Detecting Fake Reviews Through Textual-based Feature Extraction

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