Text analysis involves uncovering and extracting valuable insights from unstructured text data. It spans various tasks such as information retrieval (e.g., retrieving reports or Web site content), text classification, clustering, and more recently, entity, relation, and event extraction. Natural language processing (NLP) aims to derive comprehensive meaning from free text, essentially deciphering who did what to whom, when, where, how, and why. NLP relies on linguistic principles like part-of-speech tagging and grammatical structure analysis. As the volume of data continues to grow annually, there is a pressing need to synthesize and extract insights from vast amounts of literature. Text analysis and visualization are crucial for effective data interpretation, offering users the ability to comprehend information within constraints. In the corporate world, time is valued more than money, making quick comprehension of data essential for presentations and decision-making. At any moment the worker is instructed to provide presentation and it takes hundreds of time to apprehend what data is, for what it turned into created, what is cause the entirety has to be recognize.