Monitor and analyze the emotional impact of web content, apps, and more. Light, fast, cost-effective, and completely client-side.
MorphCast AI Emotion Statistics: Thanks to AI facial emotion recognition (FER), empower your strategy with data-driven decisions through real-time emotional statistics on your website. Integrate AI emotion statistics into your website in just one minute!
- Easy Integration: Set up in a minute and integrate seamlessly with the MorphCast Facial Emotion Recognition AI.
- Emotion Tracking: Detailed reports on arousal, valence, 98 affects, quadrants, 6 emotions, attention, and positivity.
What it can track:
- 98 affects (Russel, Sherer, Klaus model)
- Quadrants (Russel, Sherer, Klaus model)
- 6 emotions (Ekman model)
- User-Friendly: Intuitive dashboard design with options for CSV, PDF, and Json downloads.
- Device Compatibility: Access from smartphones, tablets, and computers.
- Privacy: Anonymous data collection with a customizable privacy advisory for users.
Refer to this document about “How our Emotion AI works – Responsibility, Transparency and Clarity” that explains how and what MorphCast Emotion AI can track.
In line with our philosophy of providing free and valuable tools and services, we have implemented a generous Freemium model. For more details and opportunities to get free licences, check out our pricing policy!
- Obtain your license key here, and insert it into the app which can be found here.
- Copy and paste the minimized snippet code in your page
- Enjoy your dashboard!
Note that you can use the same license key to unlock all apps.
- A revolutionary solution for understanding user emotions in real-time.
- Rapid deployment with extensive emotion tracking capabilities.
- Prioritizes user privacy with anonymous data collection. (Read our policy)
- Affordable pricing for a comprehensive solution.
MorphCast Emotion AI marks a paradigm shift in emotional tracking and offers a valuable resource for enhancing user engagement and refining content strategy.
The app is in beta. Feedback is appreciated for improving functionality and user experience.
- Russell, J., Lewicka, M. & Niit, T. (1989). A cross-cultural study of a circumplex model of affect. Journal of personality and social psychology, 57, 848–856.
- Ekman, P. (1999). Basic emotions. Handbook of cognition and emotion, 98(45-60), 16.
- Scherer, Klaus. (2005). Scherer KR. What are emotions? And how can they be measured? Soc Sci Inf 44: 695-729. Social Science Information. 44. 695-792.
- G. Paltoglou and M. Thelwall (2013), “Seeing Stars of Valence and Arousal in Blog Posts,” in IEEE Transactions on Affective Computing, vol. 4, no. 1, pp. 116-123, Jan.-March 2013, doi: 10.1109/T-AFFC.2012.36.