What is sentiment analysis?
Sentiment analysis is a technique that uses natural language processing (NLP) and machine learning algorithms to determine whether content expresses a positive, negative, or neutral sentiment. It can be applied to social media posts, news articles, stakeholder comments, patient reviews, and transcripts from conferences or advisory boards. Sentiment is often assessed at the sentence, paragraph, or document level and may also include emotion detection and topic association.
Why is sentiment analysis important in pharmaceutical strategy?
Sentiment analysis helps pharmaceutical companies understand how stakeholders perceive their products, competitors, and key scientific developments. It provides real-time insight into public and professional reactions to clinical data releases, digital campaigns, regulatory announcements, and emerging trends. The integration of large language models (LLMs) enhances the depth and accuracy of sentiment analysis by enabling more nuanced interpretation of complex medical language, context-specific phrasing, and mixed sentiment. When used in digital listening and landscaping, sentiment analysis powered by LLMs supports more rigorous stakeholder profiling, improves reputation monitoring, and informs tailored engagement strategies across medical, commercial, and communications teams.
