Open AccessA Novel Sentimental Analysis using Optimized Relevance Vector Machine Classifier
Author(s) -
N. Saranya,
R. Gunavathi
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i4.7.20536
Subject(s) - support vector machine , cuckoo search , artificial intelligence , relevance vector machine , computer science , classifier (uml) , precision and recall , machine learning , relevance (law) , recall , pattern recognition (psychology) , sentiment analysis , data mining , psychology , particle swarm optimization , political science , law , cognitive psychology
Sentimental analysis is the process of identifying the human’s thoughts or feelings. So Many methods have been developed for the sentimental analysis. Machine learning is one of the widely used approaches towards sentiment classification. In this work, Sentimental analysis is done by using Relevance Vector Machine Classifier with Cuckoo Search Optimization. Here Relevance Vector Machine Classifier (RVMC) is combined with Cuckoo Search Optimization (CSO) for better accuracy and performance. Experiment is made with movie and twitter datasets. Accuracy, precision and recall of all other techniques are evaluated. Here the comparison is made among other algorithms. The result shows that RVMC-CSO algorithm gives accuracy and good performance than other algorithm like SVM, ELM and RVM.