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High-performance sentiment classification of product reviews using GPU(parallel)-optimized ensembled methods

Sinergi · Vol. 29 No. 2 (2025) · hlm. 385–396

DOI
10.22441/sinergi.2025.2.010
ISSN
2460-1217, 1410-2331
Terbit
2025-04-14
Penerbit
Universitas Mercu Buana
Bahasa
eng

Abstrak

Sentiment analysis is an important approach in natural language processing (NLP) that extracts information from text to infer underlying emotions or views. This technique entails classifying textual information into feelings like "positive," "negative," or "neutral." By evaluating data and labeling, client input may be classified on scales such as "good," "better," "best," or "bad," "worse," resulting in a sentiment classification. With the fast expansion of the World Wide Web, a massive library of user-generated data—opinions, thoughts, and reviews—has evolved, notably for diverse items. E-commerce firms use this data to gather attitudes and views from social media sites like Facebook, Twitter, Amazon, and Flipkart. The GPU-CUDA-ENSEMBLED algorithm is a GPU-accelerated method for sentiment classification, enhancing predictive performance by minimizing variances and biases. It outperforms existing algorithms like SLIQ and MMDBM, demonstrating GPU mining's efficiency. The proposed algorithm utilizes GPU-accelerated sentiment analysis to accurately predict smartphone ratings, providing valuable insights for businesses to maximize customer feedback potential.

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Sumber metadata: OAI-PMH (metadata jurnal) https://publikasi.mercubuana.ac.id/index.php/sinergi/oai, pertama di-harvest 10 Nov 2026, terakhir terlihat 10 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">High-performance sentiment classification of product reviews using GPU(parallel)-optimized ensembled methods</dc:title>
	<dc:creator>Rao, Annaluri Sreenivasa</dc:creator>
	<dc:creator>Reddy, Yeruva Jaipal</dc:creator>
	<dc:creator>Navya, Guggilam</dc:creator>
	<dc:creator>Gurrapu, Neelima</dc:creator>
	<dc:creator>Jeevan, Jala</dc:creator>
	<dc:creator>Sridhar, M.</dc:creator>
	<dc:creator>Reddy, Desidi Narasimha</dc:creator>
	<dc:creator>Pathuri, Siva Kumar</dc:creator>
	<dc:creator>Anand, Dama</dc:creator>
	<dc:subject xml:lang="en-US">GCE</dc:subject>
	<dc:subject xml:lang="en-US">Graphics Processing Unit (GPU)</dc:subject>
	<dc:subject xml:lang="en-US">MMDBM</dc:subject>
	<dc:subject xml:lang="en-US">SLIQ</dc:subject>
	<dc:subject xml:lang="en-US">Smartphone Reviews</dc:subject>
	<dc:description xml:lang="en-US">Sentiment analysis is an important approach in natural language processing (NLP) that extracts information from text to infer underlying emotions or views. This technique entails classifying textual information into feelings like "positive," "negative," or "neutral." By evaluating data and labeling, client input may be classified on scales such as "good," "better," "best," or "bad," "worse," resulting in a sentiment classification. With the fast expansion of the World Wide Web, a massive library of user-generated data—opinions, thoughts, and reviews—has evolved, notably for diverse items. E-commerce firms use this data to gather attitudes and views from social media sites like Facebook, Twitter, Amazon, and Flipkart. The GPU-CUDA-ENSEMBLED algorithm is a GPU-accelerated method for sentiment classification, enhancing predictive performance by minimizing variances and biases. It outperforms existing algorithms like SLIQ and MMDBM, demonstrating GPU mining's efficiency. The proposed algorithm utilizes GPU-accelerated sentiment analysis to accurately predict smartphone ratings, providing valuable insights for businesses to maximize customer feedback potential.</dc:description>
	<dc:publisher xml:lang="en-US">Universitas Mercu Buana</dc:publisher>
	<dc:date>2025-04-14</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:format>application/vnd.openxmlformats-officedocument.wordprocessingml.document</dc:format>
	<dc:format>application/msword</dc:format>
	<dc:format>application/vnd.openxmlformats-officedocument.wordprocessingml.document</dc:format>
	<dc:identifier>https://publikasi.mercubuana.ac.id/index.php/sinergi/article/view/28474</dc:identifier>
	<dc:identifier>10.22441/sinergi.2025.2.010</dc:identifier>
	<dc:source xml:lang="en-US">SINERGI; Vol. 29 No. 2 (2025); 385-396</dc:source>
	<dc:source>2460-1217</dc:source>
	<dc:source>1410-2331</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://publikasi.mercubuana.ac.id/index.php/sinergi/article/view/28474/9629</dc:relation>
	<dc:relation>https://publikasi.mercubuana.ac.id/index.php/sinergi/article/view/28474/12066</dc:relation>
	<dc:relation>https://publikasi.mercubuana.ac.id/index.php/sinergi/article/view/28474/12067</dc:relation>
	<dc:relation>https://publikasi.mercubuana.ac.id/index.php/sinergi/article/view/28474/12224</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2025 SINERGI</dc:rights>
</oai_dc:dc>