Welcome back to another narrative in our quest to understand the fundamentals of Text Analytics. Now that we have laid the foundation stone with a small example in the last post, let's cut to the chase. Let's deal with the intricacies of it.
Many machine learning enthusiasts and fanatics would have already looked up for this term and undeniably there is a plethora of information on the web related to this. You might have come across terms like data mining, text analysis, and text analytics. And while to the untrained mind these might sound like synonyms, from the point of view of practice and experience, there is a subtle difference worth mentioning.
Text Analysis is the term describing the very process of computational analysis of texts. It is the automated process of understanding and sorting unstructured text, making it easier to manage and mine for valuable insights. This is very often interchanged with data mining and it is just fine to do so.
Text Analytics, on the other hand, involves a set of techniques and approaches towards bringing textual content to a point where it is represented as data and then mined for insights/trends/patterns.
TL;DR: Case in point, Text Analysis helps translate a text in the language of data. And it is when Text Analysis “prepares” the content, that Text Analytics kicks in to help make sense of these data.
Colossal amounts of unstructured data are generated every minute -- internet users post 456,000 new tweets, 510,000 new comments on Facebook, and send 156 million emails -- so managing and analyzing information to find what’s relevant becomes a major challenge.
Thanks to text analytics, businesses can automatically extract meaning from all sorts of unstructured data, from social media posts and emails to live chats and surveys, and turn it into quantitative insights. By identifying trends and patterns with text analytics, businesses can improve customer satisfaction (by learning what their customers like and dislike about their products), detect product issues, conduct market research, and monitor brand reputation, among other things.
Text analytics has many advantages – it’s scalable, meaning you can analyze large volumes of data in a very short time and allows you to obtain results in real-time. So, apart from gaining insights that help you make confident decisions, you can also resolve issues promptly.
Text Analytics is an artificial intelligence (AI) technology that uses Natural Language Processing (NLP) to transform the free (unstructured) text in documents and databases into normalized, structured data suitable for analysis or to drive machine learning (ML) algorithms. So in other words, NLP is just one of the multitude of ways used for carrying out text analytics.
NLP is growing in importance and adoption in the community of linguists because
- It is very efficient in handling large volumes of text data.
- Equally good in structuring highly unstructured data sources.
And lately, it is started delivering on the huge promises toward a seamless system.
Unstructured data: data stored in its native format and not processed until it is used eg., documents, e-mails, blogs, digital images, videos, and satellite imagery.
Computational analysis: Mathematical models used to numerically study the behavior of complex systems employing a computer simulation.
In the next article, we are going to see what are some of the popular business use cases of Text Analytics and what exactly is a typical Text Analytics pipeline (several stages of an application)?