Sentiment Analysis Framework for E-Commerce Reviews Using Ensemble Machine Learning Algorithms

Dhamayanthi, N.; Lavanya, B.

doi:10.1007/978-981-16-0171-2_34

Advances in Intelligent Systems and Computing

2021

DOI: 10.1007/978-981-16-0171-2_34

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Sentiment Analysis Framework for E-Commerce Reviews Using Ensemble Machine Learning Algorithms

N. Dhamayanthi

B. Lavanya

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2024

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Sustainable sentiment analysis on E-commerce platforms using a weighted parallel hybrid deep learning approach for smart cities applications

Vijayaragavan,

Suresh,

Maheshwari

et al. 2024

Sci Rep

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Sentiment analysis (SA) of several user evaluations on e-commerce platforms can be used to increase customer happiness. This method automatically extracts and identifies subjective data from product evaluations using natural language processing (NLP) and machine learning (ML) methods. These statistics may eventually reveal information on the favourable, neutral, or negative attitudes of the consumer base. Due to its capacity to grasp the complex links between words and phrases in reviews as well as the emotions they imply, deep learning (DL) is very useful for SA tasks. A unique approach termed Weighted Parallel Hybrid Deep Learning-based Sentiment Analysis on E-Commerce Product Reviews (WPHDL-SAEPR) is introduced by the proposed system. Accurately distinguishing between distinct sentiments found in online store reviews is the aim of the WPHDL-SAEPR technique. Additional data pre-processing processes are implemented within the WPHDL-SAEPR architecture to guarantee compatibility. Words are embedded into the paper using the word2vec model, while sentiment is classified using the WPHDL model. The Restricted Boltzmann Machine (RBM) and Singular Value Decomposition (SVD) models are combined in this model. The results of the WPHDL-SAEPR approach’s simulation were assessed using a consumer review database, with the results being emphasized at each stage.

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Sustainable sentiment analysis on E-commerce platforms using a weighted parallel hybrid deep learning approach for smart cities applications

Vijayaragavan,

Suresh,

Maheshwari

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

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Sentiment Analysis Framework for E-Commerce Reviews Using Ensemble Machine Learning Algorithms

Cited by 1 publication

References 12 publications

Sustainable sentiment analysis on E-commerce platforms using a weighted parallel hybrid deep learning approach for smart cities applications

Sustainable sentiment analysis on E-commerce platforms using a weighted parallel hybrid deep learning approach for smart cities applications

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