Uncover the attitudes behind the conversations

Healthcare Sentiment Analysis

Our sentiment analysis is powered by a natural language processing (NLP) algorithm that we’ve optimized for healthcare. It extracts subjective information from social media healthcare conversations to determine what is known as the “polarity”—expressed as positive, negative, or neutral—of specific healthcare topics. As a result, we are better able to identify varying attitudes around conversations and provide you with a deeper understanding of the associated sentiments.

Beyond the classic sentiment analysis scaling system for the three aspects of polarity, Symplur Signals empowers you to fine-tune and train our algorithm with your own proprietary customizations, thereby adapting it to your particular needs and requirements.

And, with our sentiment analysis you can drill down further and focus on attitudes and opinions specific to the different healthcare stakeholders (doctors, patients, caregivers, etc.) identified in our system.

This, together with our content filters, gives you a powerful way to derive deeper insights on healthcare topics as they relate to specific environments and stakeholders.

The Sentiment Analysis widget reveals the most positive and negative tweets. Up to 50 of the most positive and negative tweets are visualized in the graph and listed in their respective columns.

Learn more in our Help Article about Sentiment Analysis

The Social Media Analytics Platform for Healthcare

Symplur Signals performs tasks and answers questions no generic tool can because it combines the best of both worlds. Hand curation and crowdsourcing ensure unparalleled healthcare subject matter expertise; machine learning and algorithms provide endless scalability.