Amin Bandeali

• annotating data,
• the recognition of emotional-based states, and
• generating emotion-related system behavior.

According to the language authors, EmotionML can have applications in various fields like:

• Opinion mining / sentiment analysis in Web 2.0, to automatically track customer's attitude regarding a product across blogs;
• Affective monitoring, such as ambient assisted living applications for the elderly, fear detection for surveillance purposes, or using wearable sensors to test customer satisfaction;
• Character design and control for games and virtual worlds;
• Social robots, such as guide robots engaging with visitors;
• Expressive speech synthesis, generating synthetic speech with different emotions, such as happy or sad, friendly or apologetic;
• Emotion recognition (e.g., for spotting angry customers in speech dialog systems);
• Support for people with disabilities, such as educational programs for people with autism.