Sentiment Analysis with A.I. and NLP

The sentiment analysis provided in Symplur Signals is powered by a natural language processing (NLP) algorithm that we have optimized for healthcare. This proprietary algorithm extracts subjective information from social media healthcare conversations in order to determine the polarity of specific healthcare topics. As a result, we are better able to detect nuances of conversations and provide you with more accurate sentiments.

Sentiment Analysis

Learning can only take place if you are willing to listen. Discover what works and the opportunities for improvement. Symplur Signals's sentiment algorithm is optimized for healthcare.

The method used for determining sentiment employs a scaling system for the three classes of neutral, positive and negative sentiment. This method also gives you the opportunity to put your own proprietary customizations on the algorithm to adjust and finetune the results of the sentiment analysis.

Our sentiment analysis takes a step further by enabling you to focus on the sentiment specific to the different healthcare stakeholders (doctors, patients, caregivers, etc.) identified in our system. This, together with our content filters, will give you a powerful way to derive deeper insights on healthcare topics as they are related to specific environments and stakeholders.