Comparative Analysis Using Machine Learning Techniques for Fine Grain Sentiments

作     者：Zeeshan Ahmad Waqas Haider Bangyal Kashif Nisar Muhammad Reazul Haque M.Adil Khan 

作者机构：Department of Computer ScienceUniversity of GujratGujrat50700Pakistan Faculty of Computing and InformaticsUniversity Malaysia SabahJalan UMSKota Kinabalu88400SabahMalaysia Department of Computer Science and EngineeringHanyang UniversitySeongdong-guSeoul04763South Korea Faculty of Computing&InformaticsMultimedia UniversityCyberjaya63100SelangorMalaysia Department Computer Science and TechnologyChang’an UniversityXi’an710064China 

出 版 物：《Journal on Artificial Intelligence》 (人工智能杂志（英文）)

年 卷 期：2022年第4卷第1期

页      面：49-60页

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Machine learning neural network sentiment analysis system support vector machine 

摘      要：Huge amount of data is being produced every second for microblogs,different content sharing sites,and social *** classification is a tool that is frequently used to identify underlying opinions and sentiments present in the text and classifying *** is widely used for social media platforms to find user’s sentiments about a particular topic or ***,assembling,and analyzing sentiments has been challenge for *** handle these challenges,we present a comparative sentiment analysis study in which we used the fine-grained Stanford Sentiment Treebank(SST)dataset,based on 215,154 exclusive texts of different lengths that are manually *** present comparative sentiment analysis to solve the fine-grained sentiment classification *** proposed approach takes start by pre-processing the data and then apply eight machine-learning algorithms for the sentiment classification namely Support Vector Machine(SVM),Logistic Regression(LR),Neural Networks(NN),Random Forest(RF),Decision Tree(DT),K-Nearest Neighbor(KNN),Adaboost and Naïve Bayes(NB).On the basis of results obtained the accuracy,precision,recall and F1-score were calculated to draw a comparison between the classification approaches being used.