Customer data extraction techniques based on natural language processing for e-commerce business analytics

Natural language processing (NLP) is the a types of artificial intelligence approach used to maintain the decision making and data interaction process with high accuracy and reliability rate. It is also used to maintain the computer-human interaction for better understanding and result. The aim of this work is to review the data extraction techniques with NLP for a better business and user analysis process. For data analysis and user experience analysis process data analytic, K-neighbor techniques are used that are obtained using the method a lLiterature review. This process aims to review the current research articles that are focused on data extraction and analytic techniques. Besides, it is focused on NLP techniques for improving the analysis and extraction process. The Factorization, FCMA, and soft computing algorithms with NLP are reviewed that maintain precision and accuracy rate. Different tools, such as visualization, decision-making, consumer identification, and behavior analysis, are considered during the review process. In this review process, PRM and embedding matrix approaches are considered for an accurate analysis process. The data extraction, feature extraction, and machine learning model with data extraction techniques are reviewed to manage consumer experience and error estimation. This study introduces customer behavior data, Natural processing-based data extraction, e-commerce business effectiveness and evaluation as the major factors of this work.