Modeling with Net Sentiment Score
Nature of implementation
Modelling, NLP and Data Science
Nature of organization
Making sense of open-end research responses
Deep Learning, Natural Language Processing, Modeling, Data Science
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.
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.
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