How Machine Learning is Innovating Today's World

2A Review on Text Analysis Using NLP

Kuldeep Vayadande^1*, Preeti A. Bailke¹, Lokesh Sheshrao Khedekar¹, R. Kumar² and Varsha R. Dange¹

¹Vishwakarma Institute of Technology, Pune, Maharashtra, India

²VIT-AP University, Inavolu, Beside AP Secretariat, Amaravati, AP, India

Abstract

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 ...