Big data has appeared to be one of the most addressed topics recently, as every aspect of modern technological life continues to generate more and more data. This study is dedicated to defining big data, how to analyze it, the challenges, and how to distinguish between data and big data analyses. Therefore, a comprehensive literature review has been carried out to define and characterize Big-data and analyze processes. Several keywords, which are (big-data), (big-data analyzing), (data analyzing), were used in scientific research engines (Scopus), (Science direct), and (Web of Science) to acquire up-to-date data from the recent publications on that topic. This study shows the viability of Big-data analysis and how it functions in the fast-changeable world. In addition to that, it focuses on the aspects that describe and anticipate Big-data analysis behaviour. Besides that, it is important to mention that assessing the software used in analyzing would provide more reliable output than the theoretical overview provided by this essay.

Top software for Text Analysis, Text Mining, Text Analytics: Text Analysis, Text Mining, Text Analytics uses statistical pattern learning to find patterns and trends from text data.

Text analysis, also known as text mining, is the process of compiling, analyzing, and extracting valuable insights or information from large volumes of unstructured texts, using machine learning and NLP (natural language processing) techniques. The sheer volume of data available on the internet today is incomprehensible. And manually analyzing this data is not really an efficient option. To help you better understand the situation, let's look at some numbers. In 2014, there were over 2.4 billion internet users either consuming or generating content. The number grew to 3.4 billion internet users by 2016.

By some estimates, 80% of an organization's data is unstructured content. This content includes web pages, call center transcripts, surveys, feedback forms, legal documents, forums, social media, and blog articles. Therefore, organizations must analyze not just transactional information but also textual content to gain insight and boost performance. A powerful way to analyze this textual content is by using text mining. Text mining typically applies machine learning techniques such as clustering, classification, association rules and predictive modeling.

By some estimates, 80% of an organization's data is unstructured content. This content includes web pages, call center transcripts, surveys, feedback forms, legal documents, forums, social media, and blog articles. Therefore, organizations must analyze not just transactional information but also textual content to gain insight and boost performance. A powerful way to analyze this textual content is by using text mining. Text mining typically applies machine learning techniques such as clustering, classification, association rules and predictive modeling.