A Review on Text Analysis Using NLP

Summary

The application of natural language processing (NLP) methods in text analysis for information retrieval is examined in this research. First, a summary of the importance and role of text analysis in information retrieval is presented. The study then looks at text pre‐processing methods such as tokenization, stemming, and stop‐word elimination. Additionally, other NLP techniques are investigated, including sentiment analysis, part‐of‐speech tagging, and named entity identification. The following section of the study looks at several text representation models, such as word embeddings, TF‐IDF, and bag‐of‐words. Text analytics is the process of interpreting unorganized textual material and converting it into useful data for study in order to provide a measurable number that provides some crucial information. Text analysis is being used by businesses more and more. It helps with the research of unstructured information, such as customer feedback, as well as the identification of patterns and trend predictions. Solutions, databases, analysis, automated process programs, data gathering, and extraction‐based tools are only a few examples of the technology solutions for text analysis that are available for converting text data into meaningful data for analysis. This research will cover the principles of textual data, several text mining methodologies, and the most popular text analysis tools. We examine text categorization methods including Support Machines, Deep Learning and Naive Bayes, Information retrieval systems, NLP tools and libraries, text summarization strategies, and case examples in diverse disciplines, which are covered in the conclusion. An overview of text analysis for information retrieval using NLP methods is the goal of this research. Information retrieval systems, NLP tools, text summarization, text representation models, text classification, text similarity measures, and text pre‐processing are all covered in the study. The emphasis is on describing the various strategies and how they are used in information retrieval. The relevance of text analysis for information retrieval and its future possibilities are covered in the conclusion.