Semantic analysis-based relevant data retrieval model using feature selection, summarization and CNN
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METHODOLOGIES AND APPLICATION
Semantic analysis-based relevant data retrieval model using feature selection, summarization and CNN Antony Rosewelt1 • Arokia Renjit2
Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Semantic analysis is playing a major role and task in text mining process caused by the presence of huge number of relevant and irrelevant data in Internet and other resources. Here, the semantic-based text summarization must be incorporated for the successful relevant data extraction by using data classification. The accurate classification process is done by using deep learning techniques recently. However, no existing model is achieved reasonable relevancy accuracy. For overcoming the drawbacks, we propose an effective semantic analysis-based relevant data retrieval model for retrieving the relevant data from local repository or web applications in Internet. This new model consists of (i) semantic similarity-based feature selection and (ii) enrichment technique, (iii) data summarization technique and iv) text relationship-based deep neural network classifier. Here, we propose a new semantic analysis-based feature selection algorithm to select the similarity indexed relevant data from local repositories or web applications. In addition, a new semantic-based data summarization technique is also introduced for summarizing the text that is available in the online resources. Finally, a new semantic similarity-based deep neural network-based classifier is also introduced for categorizing the data according to the semantic relation. The proposed model is proved the effectiveness of the data retrieval process by conducting various experiments based on the relevant data extraction from Internet resources, and it also tested with the recognized datasets. Keywords Semantic analysis Feature selection Data summarization Classification Deep neural network Semantic similarity
1 Introduction The knowledge discovery is playing important role today due to the huge volume of available data in Internet and other resources. Many knowledge discovery models use the different text mining techniques for identifying the useful and relevant data from the volume and variety of online documents. Here, the text mining technique-based websites and the relevant data sources are diverse. All the available
Communicated by V. Loia. & Antony Rosewelt [email protected] Arokia Renjit [email protected] 1
Department of Information Technology, Jerusalem College of Engineering, Chennai, India
2
Department of Computer Science and Engineering, Jeppiaar Engineering College, Chennai, India
text mining techniques are applying the various filtering approaches for categorizing the data, online news and the technical content and the research articles, forecasting the financial status, opinion mining, sentiment and semantic analysis. These all available techniques are used to perform the text classification processes. The main aim of the text classification is
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