Emotion Markup Language - Reasons For Defining An Emotion Markup Language

Reasons For Defining An Emotion Markup Language

A standard for an emotion markup language would be useful for the following purposes:

• To enhance computer-mediated human-human or human-machine communication. Emotions are a basic part of human communication and should therefore be taken into account, e.g. in emotional Chat systems or emphatic voice boxes. This involves specification, analysis and display of emotion related states.
• To enhance systems' processing efficiency. Emotion and intelligence are strongly interconnected. The modeling of human emotions in computer processing can help to build more efficient systems, e.g. using emotional models for time-critical decision enforcement.
• To allow the analysis of non-verbal behavior, emotion, mental states that can be provided using web services to enable data collection, analysis, and reporting.

Concrete examples of existing technology that could apply EmotionML include:

• 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, fear detection for surveillance purposes, or using wearable sensors to test customer satisfaction;
• Wellness technologies that provide assistance according to a person's emotional state with the goal to improve the person's well-being;
• Character design and control for games and virtual worlds;
• Building web services to capture, analysis, and report data of non-verbal behavior, emotion and mental states of an individual or group across the internet using standard web technologies such as HTML5 and JSON.
• 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; expressive synthetic speech would for example make more information available to blind and partially sighted people, and enrich their experience of the content;
• Emotion recognition (e.g., for spotting angry customers in speech dialog systems, to improve computer games or e-Learning applications);
• Support for people with disabilities, such as educational programs for people with autism. EmotionML can be used to make the emotional intent of content explicit. This would enable people with learning disabilities (such as Asperger's Syndrome) to realise the emotional context of the content;
• EmotionML can be used for media transcripts and captions. Where emotions are marked up to help deaf or hearing impaired people who cannot hear the soundtrack, more information is made available to enrich their experience of the content.

The Emotion Incubator Group has listed 39 individual use cases for an Emotion markup language.

A standardised way to mark up the data needed by such "emotion-oriented systems" has the potential to boost development primarily because data that was annotated in a standardised way can be interchanged between systems more easily, thereby simplifying a market for emotional databases, and the standard can be used to ease a market of providers for sub-modules of emotion processing systems, e.g. a web service for the recognition of emotion from text, speech or multi-modal input.