text analytics
text analytics

Text Analytics has become a buzz word recently and rightly so.

Open ended text has always been one of the richest sources of customer feedback, in the world of Market Research. With the advent of social media and the internet, this form of feedback has taken center stage for a lot of organisations that are trying to make customer centric business decisions.

However, traversing through the various social media is sometimes impossible and often too vast to make accurate predictions. Asking the customer for direct feedback is still one of the best ways to get information that is actionable and usable in real life business context.

Even with customer feedback that is received by way of surveys, there is a huge wealth of data that can be leveraged using Machine Learning Algorithms and Predictive Analytics. Today it is possible to train machines to read and analyse text across millions of comments, quickly and accurately.

This article talks about how to use Machine Learning to analyse your open ended comments.

Why do you need Text Analytics?

Renowned analyst, Meta S. Brown, defines Text Analytics as converting text into conventional data.

It refers to the process of deriving important information from ‘unstructured’ written text. In the context of business and marketing, it usually means analysing the continuous stream of stream of free text (feedbacks and comments) either written by or about the customers.

Once a continuous data stream of customer feedback has been set up, the process of Text Analytics can then be implemented. There are essentially two things that you need to identify via Text Mining-

  1. The Overall Sentiment– whether the comments are positive or negative. The first step involves trying to understand the quality of the comment, in binary terms of Yes (for positive) and No (for negative).
  2. Cause– For instance- If a comment is positive, why is it so? This step includes figuring out what the customer is saying within the comment that classifies it as a positive or a negative statement.

Predictive Text Analytics

For Predictive Text Analytics, we first need to devise an algorithm that can scan through the textual data. This is done using Machine Learning. Numr uses an SVM (Support Vector Machine) method for this process. What it mainly does is, look at a cluster of words and try to understand which category to group them under.

But first, we need to manually categorise around a thousand comments for it to work accurately. The reason being that the Machine algorithm needs a model (based on a human perspective of classification) to base its calculations on.

Once you have categorised around a thousand comments, there are primarily two steps in the process-

STEP 1– About 70% of the categorised data is used as a Training Set. The machine then, builds a Model based on this set.

STEP 2– The remaining 30% of pre-categorised data is used as a Test Set, to check if the Model is able to predict the results accurately.

This process makes available two important pieces of information-

  1. The percentage of comments that the Predictive Model was able to classify
  2. The accuracy of that classification.
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