NLP Powered Review Management
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Overview

Currently, the client is using generic Google NLP. The difficulty in identifying and extracting hidden entities and doing sentiment scoring on them is identified as a challenge. The feedback on hidden entities affects the overall clinic’s score and reputation.

Solution Overview

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Trained a machine learning sentiment classifier to score entities as positive, negative and neutral from historic data.

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Built a custom NLP pipeline to identify and extract hidden entities in the review text and extract the sentences associated with the entities.

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The text related to the hidden entities is scored using the trained classifier.

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Trained a model to detect and extract the most common positive and negative attributes that has the highest correlation with review sentiment.

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The entities are ranked across these common positive and negative attributes.

Impact

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Produce actionable insights to their clients to identify areas of improvement and improve on them.

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Visualize trends to attain informed decisions and track the impact

Technology

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