Question: What are the six primary emotions known as the “Big6” scheme in the W3C Emotion Markup Language?
Evidence: Identifying emotion categories in text is one of the key tasks in NLP BIBREF6 . Going one step further, emotion cause extraction can reveal important information about what causes a certain emotion and why there is an emotion change. In this section, we introduce related work on emotion analysis including emotion cause extraction.
In emotion analysis, we first need to determine the taxonomy of emotions. Researchers have proposed a list of primary emotions BIBREF7 , BIBREF8 , BIBREF9 . In this study, we adopt Ekman's emotion classification scheme BIBREF8 , which identifies six primary emotions, namely happiness, sadness, fear, anger, disgust and surprise, as known as the “Big6” scheme in the W3C Emotion Markup Language. This emotion classification scheme is agreed upon by most previous works in Chinese emotion analysis.
Existing work in emotion analysis mostly focuses on emotion classification BIBREF10 , BIBREF11 and emotion information extraction BIBREF12 . xu2012coarse used a coarse to fine method to classify emotions in Chinese blogs. gao2013joint proposed a joint model to co-train a polarity classifier and an emotion classifier. beck2014joint proposed a Multi-task Gaussian-process based method for emotion classification. chang2015linguistic used linguistic templates to predict reader's emotions. das2010finding used an unsupervised method to extract emotion feelers from Bengali blogs. There are other studies which focused on joint learning of sentiments BIBREF13 , BIBREF14 or emotions in tweets or blogs BIBREF15 , BIBREF16 , BIBREF17 , BIBREF18 , BIBREF19 , and emotion lexicon construction BIBREF20 , BIBREF21 , BIBREF22 . However, the aforementioned work all focused on analysis of emotion expressions rather than emotion causes.
lee2010text first proposed a task on emotion cause extraction. They manually constructed a corpus from the Academia Sinica Balanced Chinese Corpus. Based on this corpus, chen2010emotion proposed a rule based method to detect emotion causes based on manually define linguistic rules. Some studies BIBREF23 , BIBREF24 , BIBREF25 extended the rule based method to informal text in Weibo text (Chinese tweets).
Other than rule based methods, russo2011emocause proposed a crowdsourcing method to construct a common-sense knowledge base which is related to emotion causes. But it is challenging to extend the common-sense knowledge base automatically. ghazi2015detecting used Conditional Random Fields (CRFs) to extract emotion causes. However, it requires emotion cause and emotion keywords to be in the same sentence. More recently, gui2016event proposed a multi-kernel based method to extract emotion causes through learning from a manually annotated emotion cause dataset.
[id=lq]Most existing work does not consider the relation between an emotion word and the cause of such an emotion, or they simply use the emotion word as a feature in their model learning. Since emotion cause extraction requires an understanding of a given piece of text in order to correctly identify the relation between the description of an event which causes an emotion and the expression of that emotion, it can essentially be considered as a QA task. In our work, we choose the memory network, which is designed to model the relation between a story and a query for QA systems BIBREF26 , BIBREF27 . Apart from its application in QA, memory network has also achieved great successes in other NLP tasks, such as machine translation BIBREF28 , sentiment analysis BIBREF29 or summarization BIBREF30 . To the best of our knowledge, this is the first work which uses memory network for emotion cause extraction.
In this study, we adopt Ekman’s emotion classification scheme (Ekman, 1984), which identifies six primary emotions, namely happiness, sadness, fear, anger, disgust and surprise, known as the “Big6” scheme in the W3C Emotion Markup Language.
Answer: They are namely happiness, sadness, fear, anger, disgust and surprise.