Text data the most common form of information on the Internet, whether it be reviews, tweets or web pages. Natural Language Processing (NLP) is a powerful technology that helps you derive immense value from that data. In this article, we will look at the most popular Python NLP libraries, their features, pros, cons, and use cases.
Nowadays any project must be preceded by a detailed Customer Profiling process in order to provide detailed and accurate information concerning customer’s needs and expectations. The right decision, which carries the probability of success has to be a customer-centered one, with possibly all these needs and expectations to be met.
Even a casual look at the brief Wikipedia (translated) definition proves the fallacy of such thinking: “Natural Language processing (NLP) – the interdisciplinary scientific field which joins the issues of natural intelligence and linguistics, dealing with the automatization of language analysis, understanding, translation and generating the natural language by computer.”
„Our experience has taught us that if your organization hasn’t created and thoroughly tested, repeatedly, a cyber incident response plan across all business areas and personnel, as well as performed simulations of cyber attacks, you won’t do a good job of responding when it occurs for real. We see over and over that it is very difficult to make good decisions when you’re responding to a real attack in the heat of the moment.” /David Burg, Cyber Security & Privacy Leader PwC/
It goes without saying that for the last decades a vast majority of institutions, companies, firms and the like, have dealt with the Big Data reality, which required or just forced the urgent necessity to create processing platforms capable of storing and analyzing this vast amount of data. Here is why Hadoop and [Spark](/spark-consulting/), later on, around the year 2008, came into picture.