Semantics- Reading between the Lines!

Big data analysis has made all sorts of things easier. The power of data is finally being harnessed in spades for various functions. Marketers and retailers alike are using big data to understand their consumers or customers respectively.

There are various facets of big data analytics that give it all the more power. One of the biggest data points today is the social media. All marketers accept the fact that the social media is a powerful tool to capture consumer attention.

Messaging through social media, phones, etc has become a common trend. In fact, the Queen’s English is also changing with colloquial annotations all over the world, which is called as ‘texting lingo.’

This brings to light a very important part of data analytics- text analytics. Text mining, also referred to as text data mining, roughly equivalent to text analytics, refers to the process of deriving high-quality information from text. High-quality information is typically derived through the devising of patterns and trends through means such as statistical pattern learning.

text analysis with compass

Text analysis involves information retrieval, lexical analysis to study word frequency distributions, pattern recognition, tagging/annotation, information extraction, data mining techniques including link and association analysis, visualization, and predictive analytics.

The overarching goal is, essentially, to turn text into data for analysis, via application of natural language processing (NLP) and analytical methods. Text Analysis makes qualitative research faster and easier. The ability to analyze what your respondents say helps you gain insight into their attitudes, behaviours, concerns, motivations and culture.

The technology is now broadly applied for a wide variety of government, research, and business needs. Text mining is being used by large media companies, to clarify information and to provide readers with greater search experiences, which in turn increases site “stickiness” and revenue. Additionally, on the back end, editors are benefiting by being able to share, associate and package news across properties, significantly increasing opportunities to monetize content.

Text mining is starting to be used in marketing as well, more specifically in analytical customer relationship management. It can be applied to improve predictive analytics models for customer attrition.

There is also sentiment analysis, where they may analyse movie reviews for estimating how favourable a review is for a movie. Analyzing unstructured text data is said to hold great promise. There are many text analysis tools that provide information on the readability and complexity of a text, as well as statistics on word frequency and character count.

At IntelligenceNODE, we believe that text analysis is an indispensible part of data mining and here to stay! With evolving language, our understanding of words also changes!