Automatic Text Document Classification by Using Semantic Analysis and Lion Optimization Algorithm

Text mining is a popular research area in the field of computer science and engineering that enables the processing of natural language which has applications in the area of aerospace, biomedical, and so on. Text mining unsheathes the unknown information present in the data such that the extraction of the data seems to be effective. Text classification is a subdomain of the text mining that plays a major role in labelling the documents based on their semantic meaning and context. Different Machine Learning algorithms are available to classify the available text documents. The main contribution of this paper is use of semantic analysis with Lion Optimization Algorithm and Neural Network architecture. The semantic analysis technique is used for the text classification through semantic keywords rather than using independent features of keywords in the documents. Lion Optimization Algorithm is used to adjust the weight of the Neural Network to maximize the efficiency of the classifier. Two well-known open source dataset namely 20 Newsgroups and Reuters-21578 are used for the experimentation and evaluate the performance of the classification algorithms. Significant improvement in all three performance parameters in terms of accuracy, specificity, and sensitivity is observed. The maximum values observed with our proposed algorithm are 91.86, 95.54, and 84.96 for accuracy, sensitivity, and specificity, respectively.