Published Paper Details:

The market is flooded with text mining and visualization tools that make it easier for creative individuals to find "hidden nuggets" of knowledge regarding developing technology. Every year, more and more data are produced, necessitating the need to synthesize and gather knowledge from an ever-expanding body of literature. In the corporate world, time is more valuable than money, so it takes a lot of time to comprehend what the data actually is, why it was developed, and why it is important. In this paper, we describe a case study of text analytics and data visualization using computational techniques like Natural Language Processing (NLP). This data can occasionally be found in an unstructured text format, which falls under the category of big data and has to be analyzed to yield useful information. This project uses Python libraries to process a large volume of text and provide graphical visualizations and text analysis operations such as Word cloud, Mendenhall Curve, Tokenization, Graph, Processed Text, and Name Entity Recognition (NER). The project shows that text analytics based on NLP is an effective method for comprehending the Data. To save time wastage and effort, this paper makes an effort to give a complete assessment of the data and to make appropriate data-driven judgement. This method allows the user to know the data and revise grammar simultaneously, which may be valuable for meetings, analysts, employees, students, and teachers.