Automatic Sentimental Analysis by Firefly with Levy and Multilayer Perceptron  

作　　者：D.Elangovan V.Subedha 

机构地区：[1]School of Computing,Computer Science and Engineering,Sathyabama Institute of Science and Technology,Chennai,600118,India [2]Computer Science and Engineering,Panimalar Institute of Technology,Chennai,600069,India

出　　处：《Computer Systems Science & Engineering》2023年第9期2797-2808,共12页计算机系统科学与工程（英文）

摘　　要：The field of sentiment analysis(SA)has grown in tandem with the aid of social networking platforms to exchange opinions and ideas.Many people share their views and ideas around the world through social media like Facebook and Twitter.The goal of opinion mining,commonly referred to as sentiment analysis,is to categorise and forecast a target’s opinion.Depending on if they provide a positive or negative perspective on a given topic,text documents or sentences can be classified.When compared to sentiment analysis,text categorization may appear to be a simple process,but number of challenges have prompted numerous studies in this area.A feature selection-based classification algorithm in conjunction with the firefly with levy and multilayer perceptron(MLP)techniques has been proposed as a way to automate sentiment analysis(SA).In this study,online product reviews can be enhanced by integrating classification and feature election.The firefly(FF)algorithm was used to extract features from online product reviews,and a multi-layer perceptron was used to classify sentiment(MLP).The experiment employs two datasets,and the results are assessed using a variety of criteria.On account of these tests,it is possible to conclude that the FFL-MLP algorithm has the better classification performance for Canon(98%accuracy)and iPod(99%accuracy).

关 键 词：Firefly algorithm feature selection feature extraction multi-layer perceptron automatic sentiment analysis 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]