Affiliation:
1. Thiagarajar College of Engineering, India
2. GKM College of Engineering and Technology, India
Abstract
This research work specifically focusses on the development of a predictive model for movie review data using support vector machine (SVM) classifier with its improvisations using different kernel functions upon sentiment score estimation. The predictive model development proceeds with user level data input with the data processing with the data stream for analysis. Then formal calculation of TF-IDF evaluation has been made upon data clustering using simple k-means algorithm. Once the labeled data has been sorted out, then the SVM with kernel functions corresponding to linear, sigmoid, rbf, and polynomial have been applied over the clustered data with specific parameter setting for each type of library functions. Performance of each of the kernels has been measured using precision, recall, and F-score values for each of the specified kernel, and from the analysis, it has been found that sentiment analysis using SVM linear kernel with sentiment score analysis has been found to provide an improved accuracy of about 91.18%.
Cited by
5 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Solar Cell String Defect Identification Method by Fusing CANNY Operator and HOG Operator;2023 IEEE 3rd International Conference on Electronic Technology, Communication and Information (ICETCI);2023-05-26
2. Analysis of Vocational Education Management Data Based on Immune RBF Network Model;Proceedings of the 3rd International Conference on Internet, Education and Information Technology (IEIT 2023);2023
3. Research on incremental clustering algorithm for big data;Applied Mathematics and Nonlinear Sciences;2022-12-23
4. Healthcare Multimedia Data Analysis Algorithms Tools and Applications;Digital Twins and Healthcare;2022-11-25
5. Semantic correction system using neural networks;PROCEEDINGS OF THE 3RD INTERNATIONAL CONFERENCE OF GREEN CIVIL AND ENVIRONMENTAL ENGINEERING (GCEE 2021);2021