Semantic analysis-based relevant data retrieval model using feature selection, summarization and CNN

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.