EmotionML – An Upcoming Standard for Representing Emotions and Related States

Schröder, Marc; Baggia, Paolo; Burkhardt, Felix; Pélachaud, Catherine; Peter, Christian; Zovato, Enrico

doi:10.1007/978-3-642-24600-5_35

Cited by 50 publications

(41 citation statements)

References 11 publications

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“…Works of [20,21,25] do aim to define standards in this area by defining a minimal set of basic emotions from which more complex ones can be derived or constructed by combining basic emotions. In [5] the authors develop methods to reason about emotions.…”

Section: Related Workmentioning

confidence: 99%

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Pattern-Based Emotion Classification on Social Media

Tromp

Pechenizkiy

2015

Studies in Computational Intelligence

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Sentiment analysis can go beyond the typical granularity of polarity that assumes each text to be positive, negative or neural. Indeed, human emotions are much more diverse, and it is interesting to study how to define a more complete set of emotions and how to deduce these emotions from human-written messages. In this book chapter we argue that using Plutchik's wheel of emotions model and a rule-based approach for emotion detection in text makes it a good framework for emotion classification on social media. We provide a detailed description of how to define rule-based patterns for Plutchik's wheel emotion detection, how to learn them from the annotated social media and how to apply them for classifying emotions in the previously unseen texts. The results of the experimental study suggest that the described framework is promising and that it advances the current state-of-the-art in emotion detection.

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Section: Related Workmentioning

confidence: 99%

“…For emotions however, defining a complete and clear set of emotions is much more difficult. Though several researchers attempted at defining standards in this field [20,21,25], AAAC, 1 there is still no consensus on a basic set of emotions that is generally accepted and could be objectively verified.…”

Section: Introductionmentioning

confidence: 99%

Pattern-Based Emotion Classification on Social Media

Tromp

Pechenizkiy

2015

Studies in Computational Intelligence

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“…• Onyx (Sanchez-Rada and Iglesias, 2013) is built on the same principles as Marl to annotate and describe emotions, and provides interoperability with Emotion Markup Language (EmotionML) (Schröder et al, 2011) • lemon (Buitelaar et al, 2011) defines a lexicon model based on linked data principles which has been extended with Marl and Onyx for sentiment and emotion annotation of lexical entries…”

Section: Eurosentiment Modelmentioning

confidence: 99%

“…The increasing need to deal with human factors, including emotions, on the web has led to the development of the W3C specification EmotionML (Schröder et al, 2011). EmotionML aims for a trade-off between practical applicability and scientific well-foundedness.…”

Section: Requirements For Semantic Annotation Of Sentiment In Multimementioning

confidence: 99%

A Linked Data Model for Multimodal Sentiment and Emotion Analysis

Sánchez-Rada¹,

Iglesias²,

Gil³

2015

Proceedings of the 4th Workshop on Linked Data in Linguistics: Resources and Applications

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The number of tools and services for sentiment analysis is increasing rapidly. Unfortunately, the lack of standard formats hinders interoperability. To tackle this problem, previous works propose the use of the NLP Interchange Format (NIF) as both a common semantic format and an API for textual sentiment analysis. However, that approach creates a gap between textual and sentiment analysis that hampers multimodality. This paper presents a multimedia extension of NIF that can be leveraged for multimodal applications. The application of this extended model is illustrated with a service that annotates online videos with their sentiment and the use of SPARQL to retrieve results for different modes.

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“…To ensure modularity and flexibility in integrating modules we rely on standard markup languages. Inputs and outputs of modules are represented with the XML languages: EmotionML [13], FML [14], BML [15] and MPEG-4's FAPs and BAPs [16].…”

Section: A Functional Model Of the Frameworkmentioning

confidence: 99%

A Framework for Human-like Behavior in an immersive virtual world

Kuijk

Broeck

Dareau

et al. 2013

2013 18th International Conference on Digital Signal Processing (DSP)

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Abstract-Just as readers feel immersed when the story-line adheres to their experiences, users will more easily feel immersed in a virtual environment if the behavior of the characters in that environment adheres to their expectations, based on their lifelong observations in the real world. This paper introduces a framework that allows authors to establish natural, human-like behavior, physical interaction and emotional engagement of characters living in a virtual environment. Represented by realistic virtual characters, this framework allows people to feel immersed in an Internet based virtual world in which they can meet and share experiences in a natural way as they can meet and share experiences in real life. Rather than just being visualized in a 3D space, the virtual characters (autonomous agents as well as avatars representing users) in the immersive environment facilitate social interaction and multi-party collaboration, mixing virtual with real.

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EmotionML – An Upcoming Standard for Representing Emotions and Related States

Cited by 50 publications

References 11 publications

Pattern-Based Emotion Classification on Social Media

Pattern-Based Emotion Classification on Social Media

A Linked Data Model for Multimodal Sentiment and Emotion Analysis

A Framework for Human-like Behavior in an immersive virtual world

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