SA-MSVM:Hybrid Heuristic Algorithm-based Feature Selection for Sentiment Analysis in Twitter
作者机构：Department of Computer science and EngineeringSri Ranganathar Institute of Engineering and TechnologyCoimbatoreTamilnadu641009India Department of Information and TechnologyPSNA College of Engineering and TechnologyDindigulTamilnadu624622India
出 版 物：《Computer Systems Science & Engineering》 (计算机系统科学与工程（英文）)
年 卷 期：2023年第44卷第3期
页 面：2439-2456页
核心收录：
学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)]
主 题：Bigdata analytics Twitter dataset for cloth product heuristic approaches sentiment analysis feature selection classification
摘 要：One of the drastically growing and emerging research areas used in most information technology industries is Bigdata *** is created from social websites like Facebook,WhatsApp,Twitter,*** about products,persons,initiatives,political issues,research achievements,and entertainment are discussed on social *** unique data analytics method cannot be applied to various social websites since the data formats are *** approaches,techniques,and tools have been used for big data analytics,opinion mining,or sentiment analysis,but the accuracy is yet to be *** proposed work is motivated to do sentiment analysis on Twitter data for cloth products using Simulated Annealing incorporated with the Multiclass Support Vector Machine(SA-MSVM)***-MSVM is a hybrid heuristic approach for selecting and classifying text-based sentimental words following the Natural Language Processing(NLP)process applied on tweets extracted from the Twitter dataset.A simulated annealing algorithm searches for relevant features and selects and identifies sentimental terms that customers ***-MSVM is implemented,experimented with MATLAB,and the results are *** results concluded that SA-MSVM has more potential in sentiment analysis and classification than the existing Support Vector Machine(SVM)***-MSVM has obtained 96.34%accuracy in classifying the product review compared with the existing systems.
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