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Home , Hourglass, Moral emotions

Department: and Sentiment Analysis Editor: Erik Cambria, Nanyang Technological University

The Hourglass Model Revisited

Yosephine Susanto Bee Chin Ng Nanyang Technological University Nanyang Technological University Andrew G. Livingstone Erik Cambria University of Exeter Nanyang Technological University

Abstract—Recent developments in the field of AI have fostered multidisciplinary research in various disciplines, including computer , linguistics, and psychology. Intelligence, in fact, is much more than just IQ: it comprises many other kinds of intelligence, including physical intelligence, cultural intelligence, linguistic intelligence, and (EQ).Whiletraditionalclassificationtasksand standard phenomena in computer science are easy to define, however, are still a rather mysterious subject of study. That is why so many different classifications have been proposed in theliteratureandthereisstillnocommon agreement on a universal emotion categorization model. In this article, we revisit the Hourglass of Emotions, an emotion categorization model optimized for polarity detection, based on some recent empirical evidence in the context of sentiment analysis. This new model does not claim to offer the ultimate emotion categorization but it proves the most effective for the task of sentiment analysis.

& IN 1872, CHARLES Darwin was one of the first always been a big challenge for the research scientists to argue that all humans, and even ani- community.2;3 To , in fact, there are still mals, show emotions through remarkably simi- active debates on whether some basic emotions, lar behaviors.1 Since then, there has been broad e.g., ,4 should be defined as emotions at consensus on how and why emotions have all. In this work, we do not aim to initiate any new evolved in most creatures. The definition and philosophical discussion on emotions nor to pro- the categorization of emotions, however, have pose the ultimate emotion categorization model. Our goal is simply to review some of the most popular emotion models in the context of com- puter science and, hence, propose a new version 5 Digital Object Identifier 10.1109/MIS.2020.2992799 of the Hourglass of Emotions, a categorization Date of current version 29 October 2020. model for concept-level sentiment analysis.

96 1541-1672 ß 2020IEEE PublishedbytheIEEEComputerSociety IEEE Intelligent Systems The remainder of this article is organized as identified and discussed by Steunebrink et al.,10 follows. The “Related Work” section discusses who extended the model to 24 emotion categories. the main emotion models proposed in the litera- A few after the original OCC model was ture. Later, the revised version of the Hourglass proposed, Mehrabian proposed the Valence/ model is presented in detail. Then, an evaluation model,11 a popular model in psychology of the model on three sentiment analysis data- that places specific emotion concepts in a circum- sets is provided. Finally, the “Conclusion” sec- flex model of core defined by two basic tion offers concluding remarks. dimensions: Arousal, which ranges from high to low, and Valence, which varies from positive to negative. Another very popular model, based on RELATED WORK facial expressions, was later proposed by Ekman.12 Emotion research has increased significantly The model only consists of six emotions (, over the few years thanks to the recent , , , ,andsurprise) but turned developments in the field of AI. The question, in out to be one of the most used models in the litera- fact, is not whether intelligent machines can have ture for its simplicity and applicability. Many sub- any emotions, but whether machines can be intel- sequent models are based on Ekman’s model, e.g., ligent without any.6 One of the earliest efforts in Plutchik’s wheel of emotions.13 Likewise, the - developing an emotion model was made by glass of Emotions5 is a reinterpretation of Plu- Shaver et al .7 They first selected a group of words tchik’s model for sentiment analysis. Many more and had them classified as emotion words models have been proposed in the literature,14 and nonemotion words. This step resulted in mostly to adapt previous models to different disci- 135 emotion words, which were then annotated plines, modalities, or applications. based on their similarity and grouped into cate- gories so that intercategory similarity was mini- mized but intracategory similarity maximized. REVISITED MODEL Using the typical prototyping approach, they After almost a of using the Hourglass 5 managed to develop an abstract-to-concrete model in the context of sentiment analysis, we emotion hierarchy and discovered six emotions realized that this presents several issues, namely, on the hierarchy’s lowest level: joy, , surprise, uncanny color associations; sadness, anger, and fear. This emotion study  presence of neutral emotions; implied that most emotions are fuzzy or indis-  absence of some polar emotions; tinct and they are combinations of these six basic  wrong association of antithetic emotions; emotions, which cannot be further divided.  low polarity scores for compound emotions; Later, Ortony and Turner argued against the  and view that basic emotions are psychologically absence of self-conscious or moral emotions. primitive.8 They proposed that all emotions are  discrete, independent, and related to each other through a hierarchical structure, hence there is Uncanny Color Associations no basic set of emotions that serve as the constitu- While this was not a matter that affected the ents of others. Having refuted the existence of accuracy of sentiment analysis, it has been a basic emotions, Ortony, Clore, and Collins intro- pressing issue for a while since many research- duced their own emotion model (termed OCC ers in the community questioned the choice of from the initials of the three authors).9 The OCC some colors of the Hourglass, e.g., blue for sur- model classifies emotions into 22 emotion types. prise, green for fear, and purple for both sadness The hierarchy contains three branches, namely and disgust. In line with recent studies on the consequences of events (e.g., pleased or dis- association between colors and emotions,15 we pleased), actions of agents (e.g., approving or dis- assigned tendentially warm colors to positive approving), and aspects of objects (e.g., liking or emotions and cold colors to negative ones (see disliking). A number of ambiguities of the emo- Figure 1). This also ensures a better distinction tions defined in the OCC model were later between different emotions (e.g., sadness and

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and, hence, miscategorized as neutral. This issue extended to germane emotions, e.g., and bliss, and concepts associated with them, e.g., ambition or meditation.

Wrong Association of Antithetic Emotions One of the main advantages of having an emo- tion categorization model is to be able to classify unknown concepts based on known features. For example, if the model did not contain the emotion discomfort, it could look up its opposite (comfort) and flip its polarity to obtain the polarity of the unknown concept. This mechanism works well in the new model, as emotions are now organized with respect to their polarity (see Table 2), but it generated a lot of errors in the previous version of the Hourglass, as this contained wrong associa- tions of antithetic emotions, e.g., anger and fear (which are both negative) or surprise and anticipa- tion (which are opposite in terms of meaning but not in terms of polarity). Figure 1. Hourglass model revisited. Low Polarity Scores for Compound Emotions The main goal of sentiment analysis is to cal- disgust are now blue and green, respectively) and culate the polarity value (positive or negative) of an enhanced organization of the model (positive a piece of text, an image or a video. In many appli- emotions now reside in the upper part of the cations, polarity intensity also plays an impor- Hourglass while negative ones are at the tant role for classification and decision making. bottom). The old Hourglass model had a big shortcoming in this sense: to make sure the polarity value Presence of Neutral Emotions stayed between 1 (extreme negativity) and +1 One of the main problems with the previous À (extreme positivity), a static normalization factor model was the presence of ambiguous emotions was introduced. Such a normalization factor, (e.g., distraction16) and, especially, neutral emo- however, made the polarity intensity of most con- tions, e.g., surprise. Here, we do not want to cepts very low. debate whether surprise is an emotion or not4 but we definitely do not want it in a model that is catered for sentiment analysis as this will lead to Table 1. Examples of compound emotions. the wrong categorization of all concepts (words and multiword expressions) that are semanti- cally associated with it. Surprise, in fact, only becomes polar when coupled with positive or negative emotions (see Table 1).

Absence of Some Polar Emotions Another issue with the original model was the absence of some important polar emotions, e.g., and eagerness. All the concepts associ- ated with such emotions, e.g., deep_breath or volunteer, were going undetected by the model

98 IEEE Intelligent Systems Table 2. New emotion classification with five sample emotion words for each category.

Concepts with high intensity were not the polarity formula is equal to 1, since only one ones with high emotional charge but rather dimension (Introspection) is active. The denomi- those that were associated with compound emo- nator will actually be equal to 1 for most con- tions (e.g., ) because of more dimensions cepts, as most concepts are only associated active at the same (e.g., anger and fear). with one emotion; it will be equal to 2 for con- To this end, we replaced the old normaliza- cepts that are associated with bidimensional tion factor with a new dynamic quantity that is emotions like love (joy+pleasantness) and submis- directly proportional to the number of active sion (fear+pleasantness); it will be equal to 3 for dimensions those few concepts that are associated with tri- dimensional emotions like bittersweetness (sad- I T A S p c þ c þ c þ c ness+anger+pleasantness); finally, it will be 4 for c ¼ sgn I sgn T sgn A sgn S j ð cÞjþj ð cÞjþj ð cÞjþj ð cÞj those very rare concepts that are associated (1) with compound emotions that span all dimen- where c is an input concept, p is the polarity sions like (anger+fear+sadness+disgust). value of such concept, I is the value of Introspec- tion (the joy-versus-sadness dimension), T is the Absence of Self-Conscious or Moral Emotions value of Temper (the calmness-versus-anger The old Hourglass model systematically dimension), A is the value of Attitude (the pleas- excluded what are commonly known as self-con- antness-versus-disgust dimension), and S is the scious or moral emotions such as , preju- value of Sensitivity (the eagerness-versus-fear dice, , , , or . dimension). Before, a negative concept (e.g., This has been a serious issue as it caused the death) associated with a strong emotion (e.g., model to be unable to recognize this pretty large ) would not result in a high (negative) polar- subset of emotions and, hence, the polarity (and ity because its affective intensity would have the concepts) associated with them. We solved been divided by 3. Now, that same intensity this issue by encapsulating such emotions as remains intact because the denominator of the subdimensions of Attitude (see Table 3).

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Table 3. Subdimensions of Attitude with five sample emotion words per category.

Emotions like pride and confidence, in fact, can classification problem (positive versus negative) be interpreted as positive Attitude (pleasantness and, hence, we left out models that focus on inten- and , respectively) directed at oneself. sity, e.g., the Valence/Arousal model. Likewise, embarrassment and guilt represent neg- The evaluation was performed by connecting ative Attitude (dislike and disgust, respectively) the concepts of SenticNet,20 a commonsense directed at oneself. Similarly, magnanimity and knowledge base for sentiment analysis, to a posi- sociability can be considered positive Attitude tive or negative polarity via the emotions of each (delight and pleasantness, respectively) toward model and by using sentic patterns19 to calculate others, while humiliation and malevolence repre- the polarity of each sentence in the datasets. Sen- sent negative Attitude (disgust and loathing, tic patterns model sentences as electronic cir- respectively) toward others. cuits: sentiment words are “sources” while other words are “elements,” e.g., very is an amplifier, not is a logical complement, rather is a resistor, EVALUATION but is an OR-like element that gives preference to We tested the new Hourglass model against one of its inputs (see Figure 2). Thus, for each some of the abovementioned emotion categori- emotion model, a polarity was first assigned to zation models on three sentiment benchmarks: each concept encountered in a sentence based the Blitzer Dataset,17 the Movie Review Data- on its connections with positive or negative emo- set,18 and the Amazon dataset.19 The first con- tions in the graph of SenticNet and, , sen- sists of product reviews in seven different tic patterns were used to calculate the final domains and contains 3800 positive sentences polarity of the sentence. and 3410 negative ones. The second is about As expected, the accuracy of text sentiment movie reviews and is composed of 4800 positive analysis using the models of Ekman and Shaver sentences and 4813 negative ones. Finally, is low as both are based on facial expressions the Amazon dataset contains the reviews of and, hence, cover a very limited set of emotions. 453 mobile phones, which were split into senten- Ekman’s model, in particular, is not very good for ces and labeled as positive, neutral, or negative. detecting polarity from text because, unlike The final dataset contains 48 680 negative sen-

tences and 64 121 positive ones. Table 4. Comparison of emotion models on three datasets We used these three datasets to compare for sentiment analysis. how the new Hourglass model performs on the task of polarity detection in comparison with the models proposed by Shaver,7 Ekman,12 Plutchik,13 the OCC models,9;10 and the previous Hourglass model5 (see Table 4). For this experiment, we considered sentiment analysis as a binary

100 IEEE Intelligent Systems CONCLUSION and twin disciplines have clearly demonstrated how emotions and intelligence are strictly connected. Some promi- nent researchers have also questioned the possi- bility of emulating intelligence without taking emotions into account. Emotions, however, are rather elusive entities and, hence, are difficult to categorize. Figure 2. Sentiment data flow for the sentence “The In this article, we reviewed major emotion car is very old but rather not expensive” via sentic models and proposed a new version of the Hour- patterns. glass model, a biologically inspired and psycho- logically motivated emotion categorization model Shaver’s model, it is unbalanced (as it consists of for sentiment analysis. two positive emotions and four negative ones). This model represents affective states both Plutchik’s model and the old Hourglass model through labels and through four independent performed better since they both cover 24 emo- but concomitant affective dimensions, which tions (plus compound emotions), but still suffer can potentially describe the full range of emo- from the presence of neutral emotions and the tional experiences that are rooted in any of us. absence of some important polar emotions. The The new version of the model provides a better categorization of surprise as a positive emotion, color representation of emotions; it excludes in particular, caused a lot of misclassifications neutral emotions (e.g., surprise) and includes because (at least in the context of sentiment analy- some important polar emotions that were previ- sis from product reviews) it is more often associ- ously missing (including self-conscious and ated with negative emotions, e.g., shock.Theold moral emotions); it better categorizes emotions Hourglass model performed slightly better beca- in order to ensure that antithetic emotions are use it covers eight additional compound emotions mirrored; finally, it calculates the polarity associ- that are particularly useful for polarity detection ated with natural language concepts with higher from product reviews, e.g., . accuracy. The OCC models performed considerably In the , we plan to test the validity of better thanks to the absence of surprise and the the new Hourglass model on different domains presence of some moral emotions that turned (beyond product reviews) and different modali- out to be important for sentiment analysis, e.g., ties (beyond text). We also plan to develop (as in unhappy customers regretting hav- mechanisms to dynamically customize the ing bought a product). The revisited model per- model according to different cultures, personal- formed slightly better than the original thanks to ities, group, sex, and user preferences. the addition of and disgust. Finally, the Hourglass model revisited is the ACKNOWLEDGMENTS best-performing model thanks to the better inter- The authors would like to thank Prof. A. pretation of neutral emotions like surprise and Ortony for the insightful discussions on emo- expectation and their combination with other tion categorization (especially on the inclu- polar emotions (see Table 1), the presence of sion of calmness and the exclusion of surprise) important emotions like eagerness and calmness and J. Choong, V. Choong, and A. Choong for that were missing from all other models (see their support in designing the new graphics of Table 2), and the inclusion of some moral emo- the model. tions, e.g., pride and shame, which were missing from the previous model but are important for sentiment analysis (see Table 3). Most of the mis- & REFERENCES classified sentences were using sarcasm or con- 1. C. Darwin, The Expression of the Emotions in Man and tained phrases with untriggered sentic patterns. Animals. , U.K.: John Murray, 1872.

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2. J. Panksepp, “The basics of basic emotion,” in The 17. J. Blitzer, M. Dredze, and F. Pereira, “Biographies, Nature of Emotion: Fundamental Questions, P. Ekman Bollywood, boom-boxes and blenders: Domain and R. J. Davidson, Eds., London, U.K.: Oxford Univ. adaptation for sentiment classification,” in Proc. 45th Press, 1994, pp. 20–24. Annu. Meeting Assoc. Comput. Linguistics, 2007, 3. L. F. Barrett, “Are emotions natural kinds?” Perspectives pp. 440–447. Psychol. Sci., vol. 1, no. 1, pp. 28–58, 2006. 18. B. Pang and L. Lee, “Seeing stars: Exploiting class 4. A. Ortony, “Some ‘basic’ emotions are not emotions at relationships for sentiment categorization with respect all: Surprise!” in Language and Emotion: An International to rating scales,” in Proc. 45th Annu. Meeting Assoc. Handbook, G. L. Schiewer, J. Altarriba, and B. C. Ng, Comput. Linguistics, 2005, pp. 115–124. Eds. Berlin, Germany: De Gruyter Mouton, 2020. 19. S. Poria, E. Cambria, A. Gelbukh, F. Bisio, and 5. E. Cambria, A. Livingstone, and A. Hussain, “The hourglass A. Hussain, “Sentiment data flow analysis by means of of emotions,” in Cognitive Behavioral Systems (Lecture dynamic linguistic patterns,” IEEE Comput. Intell. Notes in Computer Science), A. Esposito, et al.,Eds., Mag., vol. 10, no. 4, pp. 26–36, Nov. 2015. vol. 7403. Berlin, Germany: Springer, 2012, pp. 144–157. 20. E. Cambria et al., “SenticNet 6: Ensemble application of 6. M. Minsky, The Emotion Machine: Commonsense symbolic and subsymbolic AI for sentiment analysis,” in Thinking, Artificial Intelligence, and the Future of the Proc. 29th ACM Int. Conf. Inform. Knowl. Management, Human Mind. New York, NY, USA: Simon and 2020. Schuster, 2006. 7. P. Shaver et al., “Emotion knowledge: Further Yosephine Susanto is currently working toward exploration of a prototype approach,” J. Personality the Ph.D. degree with the Nanyang Technological Soc. Psychol., vol. 52, no. 6, pp. 1061–1086, 1987. University, Singapore. Her research interests 8. A. Ortony and T. Turner, “What’s basic about basic include affective computing and multilingual senti- emotions?” Psychol. Rev., vol. 97, no. 3, pp. 315–331, ment analysis. She received a master’s degree in 1990. applied linguistics from Atma Jaya Catholic Univer- 9. A. Ortony, G. Clore, and A. Collins, The Cognitive sity of Indonesia, Jakarta, Indonesia. Contact her at Structure of Emotions. Cambridge, U.K.: Cambridge [email protected]. Univ. Press, 1988. 10. B. Steunebrink, M. Dastani, and J. Meyer, “The OCC Andrew G. Livingstone is currently a Senior Lec- model revisited,” in Proc. Workshop Emotion Comput., turer with the University of Exeter, Exeter, U.K. His research interests lie in social identity, emotion, group 2009. processes, and intergroup relations. He received the 11. A. Mehrabian, “-arousal-dominance: A general Ph.D. degree in social psychology from the University framework for describing and measuring individual of Exeter. Contact him at [email protected]. differences in temperament,” Current Psychol., vol. 14, no. 4, pp. 261–292, 1996. Bee Chin Ng is currently an Associate Professor with 12. P. Ekman, “An argument for basic emotions,” Cognition the Nanyang Technological University, Singapore. Her Emotion, vol. 6, pp. 169–200, 1992. research interests include psycholinguistics and 13. R. Plutchik, “The nature of emotions,” Amer. Scientist, sociolinguistics aspects of language acquisition in vol. 89, no. 4, pp. 344–350, 2001. multilingual contexts. She received a Ph.D. degree in 14. Z. Wang, S. Ho, and E. Cambria, “A review of emotion linguistics from La Trobe University, Melbourne, VIC, Australia. Contact her at [email protected]. sensing: Categorization models and algorithms,” Multimedia Tools Appl., 2020. Erik Cambria is currently an Associate Professor 15. F. Takahashi and Y. Kawabata, “The association with Nanyang Technological University, Singapore. between colors and emotions for emotional words and His main research interests are AI and affective com- facial expressions,” Color Res. Appl., vol. 43, no. 2, puting. He received the Ph.D. degree in computing pp. 247–257, 2018. science and mathematics through a joint programme 16. A. Iordan and F. Dolcos, “Brain activity and network between the University of Stirling, Stirling, U.K., and interactions linked to valence-related differences in MIT Media Lab, Cambridge, MA, USA. He is the the impact of emotional distraction,” Cerebral Cortex, corresponding author of this article. Contact him at vol. 27, no. 1, pp. 731–749, 2017. [email protected].

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