AI 기반 감정 분석

Sentiment analysis works by using artificial intelligence and natural language processing (NLP) to assess the emotional tone of text or speech. It identifies key indicators of emotion such as word choice, sentence structure, and contextual cues to classify content as positive, negative, or neutral. More advanced systems can also detect intent, emotional shifts, and speaker-specific sentiment over time.

Transkriptor allows you to create custom workspaces with role-based permissions for organizing sentiment analysis projects. You can also build knowledge bases using sentiment-tagged content, making it easy to store, categorize, and search through emotional data while maintaining security and accessibility.

Sentiment analysis helps customer service teams identify emotional triggers in conversations, measure the effectiveness of de-escalation techniques, and track customer sentiment throughout the support process. These insights enable teams to refine their approach, leading to improved customer satisfaction and reduced repeat calls.

Yes, Transkriptor supports sentiment analysis in over 100 languages with high accuracy. The system is designed to recognize cultural and linguistic nuances specific to each language, ensuring consistent sentiment detection regardless of the speaker's native language.