A Parallel Approach for Sentiment Analysis on Social Networks Using Spark

作     者：M.Mohamed Iqbal K.Latha 

作者机构：Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai600062India University College of EngineeringAnna University(B.I.T Campus)Tiruchirappalli620024India 

出 版 物：《Intelligent Automation & Soft Computing》 (智能自动化与软计算（英文）)

年 卷 期：2023年第35卷第2期

页      面：1831-1842页

核心收录：

学科分类：081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Social networks sentiment analysis big data spark tweets classification 

摘      要：The public is increasingly using social media platforms such as Twitter and Facebook to express their views on a variety of *** a result,social media has emerged as the most effective and largest open source for obtaining public *** node computational methods are inefficient for sentiment analysis on such large *** or parallel or distributed proces-sing are two options for dealing with such large amounts of *** parallel programming frameworks,such as MPI(Message Processing Interface),are dif-ficult to use and scale in environments where supercomputers are *** the Apache Spark Parallel Model,this proposed work presents a scalable system for sentiment analysis on Twitter.A Spark-based Naive Bayes training technique is suggested for this purpose;unlike prior research,this algorithm does not need any disk *** of tweets have been classified using the trained *** with various-sized clusters reveal that the suggested strategy is extremely scalable and cost-effective for larger data *** is nearly 12 times quicker than the Map Reduce-based model and nearly 21 times faster than the Naive Bayes Classifier in Apache *** evaluate the framework’s scalabil-ity,we gathered a large training corpus from *** accuracy of the classi-fier trained with this new dataset was more than 80%.