The concept of emotion markup language itself is quite fascinating. In one of our web data mining applications, we are trying to deal with sentiment based on discussion groups. Easier said than done. There are several packages that mine social data for sentiments. So I was hoping that some kind of standard for Sentiment, at least a vocabulary or an ontology would show up.
But I was not prepared for this one. Emotion Markup Language? Then it rang a bell. I vaguely recall seeing this a while ago.
Take a look at the goals and some applications as stated in the document. Remember that it is just the beginning.
As for any standard format, the first and main goal of an EmotionML is twofold: to allow a technological component to represent and process data, and to enable interoperability between different technological components processing the data.
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 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.
As I mentioned earlier, sentiment mining has already started happening even though still in its nascent stages. It will be interesting to watch this space.