Culture independence vs context dependency- Ekman’s “dangerous” theory

, Precognox

This post is part of a case study of emotion analysis focusing primarily on the theoretical background of text based emotion representation.

Here I wish to point out that exploring the field of text based emotions may reveal information otherwise inaccessible in sentiment analysis. Therefore it may even result in a different kind of benefit that enhances its value.

In order to find out what kind of emotions are “hiding” in texts it is first needed to be defined what we are actually looking for. The simplest solution seems to search for linguistic expressions explicitly indicating a certain emotion. Let’s take a look at some real-life examples:

1 XDDDDDDD well, you know even an innocent smiley can freak you out 🙂

2 Still terrified, the actress turned to the public.

The highlighted items seem worth collecting and adding to a dictionary based on the emotions they express. In order to do that however first the system of categorization need to be defined. The next obvious step for a linguist therefore is to check what psychology has to say about which emotion categories are worth the time.

The method above is the so-called current beaten track of emotion analysis– if such a track exists at all considering the insignificant number of international and Hungarian publications. While searching for the relevant psychological data the language technologist comes across Paul Ekman’s theory. According to Ekman there are six basic emotions– sadness, anger, fear, surprise, happiness and disgust– the facial expressions of which are universal, i.e. independent of the person’s cultural background and mean the same emotional state for everyone.


In the 1970s Ekman and Friesen developed the Facial Action Coding System (FACS) to taxonomize every human facial expression. The method, which is the result of decades of research, describes all observable facial movements for every emotion and by analysing them it determines the emotional state of the person. The fact that both genuine and fake emotions can be precisely identified is the eloquent proof of its reliability.

No wonder Ekman was named one of the top 100 most influential people in the May 2009 edition of Time magazine.


Paul Ekman and Tim Roth, the star of the TV series “Lie to me”.


The widespread popularity of this categorization provided a solid base for emotion analysis in language technology as well. Most relevant studies categorize emotion expressions either directly based on Ekman’s theory (Liu et al. 2003; Alm et al. 2005; Neviarouskaya et al. 2007 a,b; Aman-Szpakowicz 2007) or like us take it for their basis adding some other classes as well e.g.: attraction or tension (Szabó et al. 2015). The argument that these emotions are universal is so convincing that computational linguists almost forget to ask whether this is the very feature they need at all or if this otherwise important fact disguises features which should be an essential part of the analysis?

As I promised in the title of the post I intend to write about Ekman’s “dangerous” theory. I am referring to the book “Darwin’s Dangerous Idea: Evolution and the Meanings of life (1995)” by Daniel C. Dennett here and also drawing a parallel with Ekman’s theory. According to Dennett there are two reasons why Darwin’s theory may be dangerous: First, because his thoughts questioning the privileged role humans were said to enjoy in the universe profoundly shook the foundation of the traditional cosmological approach. He also doubted that life itself should actually have a peculiar ontological status. Second, according to Dennett Darwin’s theory is easy to misunderstand therefore it may generate dangerous misinterpretations. The reason why Ekman’s theory- the ability to read emotions on faces is innately hardwired- is “dangerous” is that it’s so convincing that other aspects of expressing emotions– like facial or linguistic– are easily ignored. One important factor is the role of context in the interpretation of emotions, and it is not exclusively about text analysis.  Let us take a closer look at the phenomenon:

In their article– Language as context for the perception of emotion, 2007– Barrett and her co-authors challenge the idea of innate emotion perception by using a certain photo as an example. The photo was taken of United States Senator Jim Webb celebrating his 2007 electoral victory. Experiments revealed when subjects saw the image of the Senator taken out of context (see image a.) they all said he looked angry and aggressive. When situated however in the original context subjects agreed that he appeared happy and excited.

The result is remarkable considering that not once did the subjects find the senator’s facial expression misunderstandable or confusing but came to the conflicting conclusions automatically and effortlessly.


Barrett (Barrett at al. 2007) considers this phenomenon a paradoxon since it’s rather controversial that there are six facial expressions which are biologically perfectly distinguishable but their interpretations may be absolutely context-dependent. The authors try to come up with an explanation such as words ground category acquisition, but in my opinion this argument is not convincing enough.

In exchange for the Ekman categories here linguistics seems to lend psychology a conceptual framework which needs to be traced back as far as Wilson and Sperber’s Relevance theory (2004). It argues that in any given communication the hearer or audience will search for meaning and having found the one that fits their expectation of relevance will stop processing. In the conceptual framework of lexical pragmatics it all means that the lexeme itself is nothing but an underspecified semantic representation. Consequently it gains its complete meaning only in context (Bibok 2014). Where does this underdetermined meaning come from? Obviously there must be a pragmatic knowledge embracing all information necessary for code development.

As all this sounds rather complicated let us demonstrate how the theory works with an example from the field of sentiment and emotion analysis.

3.a. Suspect of bestial double murder in custody. (

b An American lady had a formidable experience while taking part in a shark cage watch program in Mossel Bay, South-Africa. (

4 Debut of a bestial Volkswagen GTI Supersport Vision Gran Turismo (…) A formidable fastback implementing other aspects of the “GTI” concept.(

According to the idea introduced above in sentences 3a and 3b understanding the highlighted words is based on encyclopaedic information stored in our pragmatic knowledge. This means we have some kind of an idea based on our previous experience of what something bestial or formidable is like. This is basically the encyclopaedic information stored in the underspecified semantic representations of the expressions in question. Using these pieces of information we can find out what they meant to express in the given context. In sentence 4 this encyclopaedic information is not perfectly in line with the current context so the encyclopaedic information in the underspecified semantic representation is not enough and therefore „further” information is necessary. In example 4 the “further” information is the emotive feature of the expressions “bestial” and “formidable”. Consequently we can say that in a situation like this during interpretation it’s the semantic feature indicating emotion or intensity of the studied lexemes that gets activated instead of the prototypical or stereotypical meaning. Put more simply: we don’t think that the new Volkswagen is as bestial as a murder and we need to be scared but instead we know that it’s as effective, impressive and surprising as the amount of emotiveness the phrases “bestial” and “formidable” have.

Considering this process of interpretation a certain parallel may easily be detected between expressing emotions at a textual level and understanding the emotional information faces display. It is evident how these two processes are similar; we are able to interpret the word “bestial” correctly in a context where this interpretation is required based on the sheer emotive semantic features and ignore its prototypical or stereotypical meaning. We are also able to interpret the face of the senator displaying the obvious signs of anger as the expression of excitement and joy if this is the interpretation the context requires.

Although obviously exciting and remarkable in itself I did have a specific reason to discuss the theoretical parallel above. My primary goal was to point out that while emotion analysts (and let’s face it: sentiment analysts as well) often focus on categories, their problems and possibilities, they sometimes forget about significant aspects like the role of context in the interpretation of linguistic- in case of facial expressions non-linguistic- signs. As a result a relevant psychological theory that can successfully be applied in linguistics may easily become “dangerous”.


Alm, C.O.-Roth, D.-Sproat, R. 2005. Emotions from text: machine learning for textbased emotion prediction. In Proceedings of the Joint Conference on Human Language Technology / Empirical Methods in Natural Language Processing (HLT/EMNLP 2005). Vancouver, Canada. 579-586.

Aman, S.-Szpakowicz, S. 2007. Identifying Expressions of Emotion in Text. In Proceedings of the 10th International Conference on Text, Speech, and Dialogue (TSD- 2007), Plzeň, Czech Republic, Lecture Notes in Computer Science (LNCS). SpringerVerlag. 196-205.

Barrett, L.F.-Lindquist, K.A.-Gendron, M. 2007. Language as context in the perception of emotion.Trends in Cognitive Sciences 11. 327-332.

Bibok, K. 2014. Lexical semantics meets pragmatics. Argumentum 10. Debrecen University Press 221-231.

Ekman, P.-Friesen, W.V. 1969. The repertoire of nonverbal behavior: Categories, origins, usage, and coding. Semiotica 1. 49-98.

Ekman, P.-Friesen, W. V.-Ellsworth, P. 1982. What emotion categories or dimensions can observers judge from facial behavior? In P. Ekman Ed. Emotion in the human face. New York: Cambridge University Press. 39-55.

Liu, H.-Lieberman, H.-Selker, T. 2003. A Model of Textual Affect Sensing using Real World Knowledge. In Proceedings of the International Conference on Intelligent User Interfaces, IUI 2003, Miami, Florida, USA.Wilson, D.-Sperber, D. 2004. Relevance Theory. In Ward, G.-Horn, L. eds. Handbook of Pragmatics. Oxford, Blackwell. 607−632.

Neviarouskaya, A.-Prendinger, H.-Ishizuka, M. 2007a. Analysis of affect expressed through the evolving language of online communication. In Proceedings of the 12th International Conference on Intelligent User Interfaces (IUI-07). Honolulu, Hawaii, USA. 278-281.

Neviarouskaya, A.-Prendinger, H.-Ishizuka, M. 2007b. Narrowing the Social Gap among People involved in Global Dialog: Automatic Emotion Detection in Blog Posts, In Proceedings of the International Conference on Weblogs and Social Media (ICWSM 2007). Boulder, Colorado, USA. 293-294.

M.K.Szabó– V. Vincze– G. Morvay 2015. Challenges in theoretical linguistics and language technology of Hungarian textbased emotion analysis. Language– Language technology– Language Pedagogy 21st century outlook 25 MANYE Congress, Budapest

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