NER algorithms enable sentiment analysis in social listening data by extracting important entities from direct comments, brand mentions and other user-generated content. This enables you to measure what customers love about your brand and where to improve.
NER is also critical in tracking brand reputation. It helps AI tools identify negative brand mentions as and when they occur in social comments and DMs. This enables your team to be proactive and concentrate on taking relevant actions to resolve issues rather than spend time manually monitoring your brand health.
Screenshot of Sprout's sentiment analysis report indonesia mobile database showcasing negative and positive sentiment trends over time periods including net sentiment scores and net sentiment trends.
NER is widely used across industries to identify important entities in keywords, topics, aspects and themes in text sources to provide impactful summaries. These text sources include news articles, podcasts, legal documents, movie scripts, online books, financial statements, stock market data and even medical reports. NER plays an important role in how AI generative tools interpret queries or prompts. Click on the link to learn how to maximize the output from AI tools by using the best ways of writing AI prompts.
Summaries from these sources can serve strategic purposes such as brand reputation management, patient experience (PX) analysis or gauging a company’s financial performance over time.