With the growing amount of data in recent years, that too mostly unstructured, it’s difficult to obtain the relevant and desired information. It’s also tedious, time-consuming, and therefore expensive, and manually sorting through large amounts of data is more likely to lead to mistakes and inconsistencies. Plus, it doesn’t scale well.

But the recent developments in the field of Natural Language Processing (NLP) have helped to mine through the data and gather important insights that help in better decision making.
One such development is Topic modeling. It is a technique that allows you to automatically extract meaning from texts by identifying recurrent topics or themes. It allows you to sift through large sets of data and identify the most frequent topics in a very simple, fast and scalable way.

In the healthcare IT domain, one deals with client survey data regularly. Having to analyze the open-ended survey responses is a big challenge for any organization but it is important as
1. Allow an infinite number of possible answers
2. Gain (unexpected) insights
3. Understand how your respondent thinks
4. Give you qualitative data
5. Will give you opinions and feelings, adding value to the answer

The purpose of this talk is to give a brief overview of how client survey data was used to streamline the process in the below areas of
1. Client satisfaction & loyalty
2. Product & service enhancements
3. Operational Efficiency
4. Benchmarking for development

Create a score based on words in topic to make subjective topics turning into objective outcomes.