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Jeevan, Jala
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Civil Engineering, Building, Construction & Architecture Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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High-performance sentiment classification of product reviews using GPU(parallel)-optimized ensembled methods Rao, Annaluri Sreenivasa; Reddy, Yeruva Jaipal; Navya, Guggilam; Gurrapu, Neelima; Jeevan, Jala; Sridhar, M.; Reddy, Desidi Narasimha; Pathuri, Siva Kumar; Anand, Dama
SINERGI Vol 29, No 2 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2025.2.010

Abstract

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.
Co-Authors Anand, Dama Gurrapu, Neelima Navya, Guggilam Pathuri, Siva Kumar Rao, Annaluri Sreenivasa Reddy, Desidi Narasimha Reddy, Yeruva Jaipal Sridhar, M.