Making sense of open-end research responses - Case Study

Making sense of open-end research responses - Case Study

Modeling with Net Sentiment Score

Nature of implementation

Modelling, NLP and Data Science

Nature of organization

Making sense of open-end research responses

Industry

Market Research

Capabilities Utilized

Deep Learning, Natural Language Processing, Modeling, Data Science

Technologies


Background & Case

In market research, open end responses are not analyzed beyond word cloud. However, open-ended responses have potential for imparting key insights about consumer behavior and product/brand/topic perception.​

For a leading market research company, the need was to get to the important topics in the textual data and then calculate net sentiment score.​

Solution

An example of an analytics engine

We developed state of the art generic text analytics engine which could identify the most important topics and subtopics in the give large textual data (feedback, responses etc.)

Value & Impact

With this solution in place, client had an edge over other solutions to apply filters on textual data along with seeing the net sentiment score. This helped them to identify what was driving the negative and positive sentiment in their services, real-time​.​

Tooliqa specializes in AI, Computer Vision and Deep Learning to help businesses simplify and automate their processes with our strong team of experts across various domains.

Want to know more on how AI can result in business process improvement? Let our experts guide you.

Reach out to us at business@tooli.qa.

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