EmotiOn: An Ontology for Emotion Analysis

Transcription

1 EmotiOn: An Ontology for Emotion Analysis Huma Tabassum Department of Computer Sciences Bahria University Karachi Campus Karachi, Pakistan Sohaib Ahmed Department of Computer & Software Engineering Bahria University Karachi Campus Karachi, Pakistan Abstract Emotions are fundamental to human nature. Automatic emotion analysis has gained much attention from researchers over the past few years. The reason is continuous increase in communication over the web. This study proposes an ontology for emotion analysis, called EmotiOn, based on Plutchik s wheel of emotions. Ontology captures the intrinsic knowledge of any domain in an easy to understand, machineinterpretable standard formalization. It is believed that EmotiOn can be used in a wide variety of applications. The results show that the ontology is consistent and functional, and ready to be used for emotion analysis. Keywords emotions; emotion analysis; ontology I. INTRODUCTION Emotions have always mystified scientists, researchers, and philosophers since early days. The initial attempts to describe emotions date back to ancient times. In the modern era, scholars have tried to comprehend emotions, its causes and effects. Various theories and models have been proposed in order to represent and categorize emotions over the years. Arnold based her classification of emotions on appraisal theory developed over cognition [1], while Ekman built his model on facial expressions [2]. Plutchik, in contrast, formulated a wheel of emotions over biological processes that arise from, and result in, adaptation to different situations that serve as stimuli [3, 4]. It provides a comprehensive model to represent emotions, and has thus been used as a source for this study. Emotion analysis is the study of detecting and investigating emotions. Since electronic communication is increasing day by day, much attention is being paid to analyze emotions automatically. However, most of the existing studies have focused on sentiment analysis instead. Sentiment analysis classifies the input as positive or negative; that is, focus is primarily on polarity, whereas emotion analysis works at a finer granularity [5]. For a negative sentiment, emotion gives description as anger, sadness, or fear. This shows that emotions are better understood through some representative model that requires some formal description. Hence, this study focuses on emotion analysis. Ontology provides a means to specify the concepts of any domain under consideration, along with the relationships among the various concepts. The main advantage of using ontology is that it is comprehensible by both humans and computers. The organization is such that it forms a structure for description. It also offers benefits like reusability and generality, other than the shared understandability [6]. For these reasons, this study revolves around ontological approach. Since little work has been done for performing emotion analysis through ontologies, this study proposes an ontology EmotiOn for this purpose. Design methodology for EmotiOn follows the one given by Uschold and King [7]. It provides a four step process for developing ontologies, and has been adopted by various studies [8]. For evaluation purposes, several Description Logic (DL) [9] queries were formulated. These queries were used to check the correctness and consistency of the ontology. The organization of the rest of the paper is as follows. Section II discusses emotion analysis along with Plutchik s wheel of emotions. Section III gives an overview of the exiting ontologies in emotion analysis. The development methodology is given in section IV, while the proposed ontology is described in section V. Section VI discusses the evaluation, and conclusion and future work are given in section VII. II. EMOTION ANALYSIS Emotion analysis allows us to study and extract emotional information from the any available content. This content can be images and videos showing facial and other bodily expressions [10], voice and speech [11], physiological and neurological data and scans [12], or text [13]. Text has always been the most widely available sources of data. Although extracting emotions from text is a daunting task, several studies have attempted to analyze it through different ways. Studies for automatic emotion analysis can be categorized into appraisal-based [14], corpus-based [15], and knowledgebased models [15]. Appraisal-based models are based on the appraisal theory in psychology [14]. It describes emotions on the basis of cognition. According to appraisal theory, our reaction to any event or situation determines the emotions we experience [16]. There are some studies which have used appraisal theory for emotion detection and analysis. One such study was given as EmotiNet [17]. Corpus-based studies include methods such as Keyword Spotting, Lexical Affinity, and Statistical Natural Language Processing [14]. Keyword spotting uses emotive words (happy, sad, etc.) to identify emotions. This is a simple method, but does not perform well in the absence of such explicit words [14]. Lexical affinity associates a probability of emotion with each word in order to determine its affinity to an emotion. For

2 example, the word furious will have a higher probabilistic affinity towards anger than joy. Example of a study using this method was given by [18]. Performance of this method decreases with rhetorical sentences. Also, assignment of probabilities is not easy, and conflicting. Thus, an accepted corpus with high agreement is required. Statistical natural language processing employs machine learning techniques to obtain affinities, word co-occurrence frequencies, and many other features for classification. This technique has been used by [5]. However, this technique requires a large training dataset, and does not perform well for short inputs (tweets, comments, posts, etc.) which lack linguistic structure. Knowledge-based studies can be primarily categorized into two techniques; Latent Semantic Analysis (LSA) [15] and Semantic Web approaches [13], of which ontology is the most prominent approach. LSA is a vector space model used to represent relations between terms or words. It calculates semantic similarity between terms and use it to classify a document or retrieve similar ones [19]. This method, again, requires a huge dataset to first extract all relevant terms to form vectors before performing emotion analysis. An overview of ontologies is given in section 3. A structured representation of the domain usually aids in construction of ontology. As mentioned earlier, emotions are also better understood and explored through some structured representative model. It is why this study is based on one such model, called Plutchik s wheel of emotions [3, 4]. The model is discussed next. Plutchik s Wheel of Emotions Plutchik s wheel of emotions is a prominent model for the representation of emotions [20]. The model identifies 8 primary or basic emotions; Anger, Anticipation, Joy, Trust, Fear, Surprise, Sadness, and Disgust. It intricately places semantically related emotions together in a circle, exactly across its opposing emotion. The wheel is shown in fig. 1. It is evident from the figure that Sadness is placed opposite to Joy. Fig. 1. Plutchik's Wheel of Emotions as cited in [20] The wheel was designed in such a way that the emotions were placed according to three intensity levels in concentric circles. Each emotion was presented with a different color, with its varying shades denoting the intensity levels along the radius. The primary/basic emotions were placed in the middle circle, with its higher intensity equivalents in the inner circle, and its lower intensity equivalents in the outer circles. For example, Anger is a basic emotion shown with red color. Its milder equivalent is Annoyance placed in outer circle with a lighter shade of red, and its intense equivalent Rage in a darker shade of the same color in the inner circle. As we move away from the circles, it can be observed that there are some emotions placed between two emotions. These are the complex emotions formed by a combination of the two adjacent primary emotions. For example, Optimism is constituted from Anticipation and Joy. III. ONTOLOGIES FOR EMOTION ANALYSIS Ontology is a term originating from psychology, but it started being used in computer science in 1980s. The most popular definition of ontology was given by Gruber as explicit specification of a conceptualization [21]. It meant that ontologies give a proper description of concepts, real world concepts, for any domain that they are being constructed for. This definition was later modified and upgraded to a more formal one by various scholars such as [7]. However, there has been some agreement on basic components such as a domain under consideration, and its concepts identified through relationships between them. Ontologies work really well with text. Just as ontology uses formal language to represent a certain domain, humans use natural language to describe that domain [22]. Ontologies have been used in a wide range of application areas. These include study of genes [23], intelligent tutoring systems and learning [24], and collaborative software development [25], to name a few. There are a number of benefits of using ontology. It provides many attractive advantages such as reusability, generality, shared understandability, etc. It is also costeffective and flexible. This means that initially, an ontology can be created as a model to depict some real world domain, and then matured incrementally. It becomes easy to add new concepts and modify the existing ones and their relationships without having to re-create [6]. This is why an ontology model is chosen for this study, so that in future it can be extended and adapted for any domain. Although, there are not many studies available which have employed the use of ontology for emotion analysis, it seems quite intuitive. A structure that provides formal description to a standard representation of real-world concepts can actually help improve understanding of these concepts. The lack of existing research gives an opportunity to explore this area. In a study [13], they developed an emotion ontology called OntoEmotion over these emotions; sadness, happiness, anger, fear, and surprise. This ontology was constructed over emotions and two languages, namely English and Spanish, in which they occur. They concluded that using ontology improves results of an automatic system for emotion

3 recognition. However, their ontology was not based on some representative model of emotions. They tried to incorporate different emotional structures into one ontology, and as a result the basic emotions in their taxonomy become dubious. Also, the number of sublevels of these emotions leading to specific emotions was varied. This lead to ambiguity in association of different emotional concepts in the hierarchy. Their application EmoTag associated Terror with Panic as parent, rather than Fright. Categorizing according to one emotion model may violate another theory. In another study by [26], a visual ontology over the Plutchik s wheel of emotions [3, 4] was proposed. This ontology was designed especially for images using adjectivenoun pairs. Although the ontology was constructed over Plutchik s wheel, it did not completely capture the model. It was used for performing sentiment analysis, and associated emotions with the same. Using adjective-noun pairs to find representative images for emotions requires careful annotation to avoid mistakes, and is fairly cumbersome. Such possible pairs are quite large in number. Also, the use of just adjectives and nouns does not capture all emotion words. Verbs and adverbs carry emotional content as well; for example, rejoice is a verb that represents emotion the Joy. Due to the reasons mentioned above, a new ontology EmotiOn is presented in this paper. EmotiOn captures Plutchik s wheel comprehensively. The next section describes methodology for design of EmotiOn. IV. METHODOLOGY The methodology for the construction of this ontology follows the one given by Uschold and King [7]. Fig. 2 shows the steps for the methodology used in the development of EmotiOn. This methodology came into existence as a result of the development of an enterprise level ontology [7]. This benefit of this approach is that it is application-independent, and somewhat resembles development of knowledge-based systems [8]. It has a formally defined four step procedure that clearly separates each phase of development. These steps with regards to our ontology are described below. A. Identify Purpose The first step was to identify and establish the need for building a new ontology. The motivation has been described in the previous section. Also, the intended use and expected outcomes of the ontology were specified. B. Building the Ontology 1) Ontology Capture This was the most crucial phase. In this step, an in-depth understanding of the domain was sought through various means. The scope, various concepts, and relationships were identified, along with the proper terminologies of the domain. For each concept and relation, definitions were produced. 2) Coding After the above requirements were completed, the ontology Fig. 2. Methodology for Development of Ontology [7] was formally represented using OWL2 1 (Ontology Web Language). It is the recommended language for building ontologies recognized by W3C 2 (World Wide Web Consortium). It is a highly expressive language that allows portrayal of concepts in a comprehensible taxonomy. It is an extension of OWL whose subset, OWL-DL, is based on Description Logic, which provides complete expressivity with good reasoning capabilities, without complicating the semantics [9]. This is why, it was chosen for creation of EmotiOn. Protégé was used as the ontology editor, which is the most widely used tool for creating ontologies [27]. It has a user-friendly interface that allows easy creation and visualization of ontologies. It is flexible in the sense that various plug-ins can be added to it for enhanced functionality with ease. 3) Integrating Existing Ontologies According to [7], this step decides whether to use an existing ontology, and integrate it with the one being built. Since EmotiOn was being created as a new representative ontology, this step was not performed. C. Evaluation The next step was to evaluate the ontology against the requirements and goals, specified in previous steps. This was done with the help of a reasoner. Protégé provides two built-in reasoners namely, HermiT and Fact++. These not only determine consistency but also infer relationships between classes that have not been explicitly stated. For this research, HermiT was used to check consistency of the proposed ontology. A description of evaluation is given in section VI

4 D. Documentation In the last step, a brief document was compiled for quick reference. Since there are no guidelines available for documenting ontologies [8], creation of a detailed document may be taken up in future. V. EmotiOn: THE PROPOSED ONTOLOGY This ontology covers the entire Plutchik s wheel of emotions [3, 4] and is named as EmotiOn. The structure of the ontology comprises of three main classes; Emotion, Neutral, and Intensity as depicted in Fig. 3. The ontology also contains three object properties; hasintensity, isoppositeof, and iscomposedof. A. Classes Classes represent the main entities and concepts of the domain. Emotion is the most important class of this ontology. It contains four subclasses, namely, Intense Emotion, Basic Emotion, Mild Emotion, and Complex Emotion, each containing eight subclasses for a total of 32 classes. Intense Emotions are the emotions which have high intensity, and were shown in the wheel with darkest shade of colors. Basic Emotion contains those primary emotions whose intensity is normal, and were presented in the wheel with lighter shade than that of Intense Emotions. Mild Emotion has the emotions with low intensity, and were depicted in the wheel with lightest shade of the same color for emotions. Complex Emotion comprises of those emotions that are formed by a combination of two adjacent primary emotions (Basic Emotion) on the wheel. OWL2 has such a syntax that encloses each entity through <Declaration> </Declaration> element, and allows prefixing the IRI 5 (Internationalized Resource Identifier) of the ontology. IRI is an identifier that uniquely recognizes each element of an ontology. For any IRI of an ontology any entity is identified by its name followed after the character #. Prefixing helps to use the IRI subsequently throughout the ontology for adding new entities without having to re-specify it each time. Examples of declaration of a few classes in Emotion is given below. <Declaration> <Class IRI= #BasicEmotion /> </Declaration> <Declaration> <Class IRI= #Emotion /> </Declaration> <Declaration> <Class IRI= #Joy /> </Declaration> Note that these are just the declarations. Any other information or relation, such as subclasses and disjoint classes, etc. are specified within their own respective elements. Examples of each are as follows. <SubClassOf> <Class IRI="#BasicEmotion"/> <Class IRI="#Emotion"/> </SubClassOf> <SubClassOf> <Class IRI="#Joy"/> <Class IRI="#BasicEmotion"/> </SubClassOf> Each subclass is defined under <SubClassOf> element. The first <Class> element identifies the subclass and the second <Class> element is the parent/super class. Similarly, the disjoint classes, that is those which are distinct from each other are given by <DisjointClasses> element as <DisjointClasses> <Class IRI="#Joy"/> <Class IRI="#Sadness"/> </DisjointClasses> The class Neutral was not originally a part of Plutchik s wheel of emotions. However, it has been included in the ontology to model real world. Usually, when humans communicate, most of the words do not contain any emotion. It is also observed that the presence of opposing emotions cancels the effect of each other, and the resulting net emotion cannot be categorized in any class. Thus, Neutral should not, in any way, be interpreted as lack of emotions. The Intensity class has three subclasses; High, Normal, and Low. These represent the intensity levels of emotions contained in any class of Emotion. The High intensity is associated with Intense Emotion, Normal with Basic Emotion, and Low with Mild Emotion through an object property. B. Properties Properties relate two entities/objects in an ontology to one another. In the proposed ontology, hasintensity object property that associates every class in Emotion to one of class Intensity. In OWL2, properties are identified as <ObjectProperty IRI="#hasIntensity"/> For the assertion of a property on some class, the syntax is given by the example below. <Class IRI="#Joy"/> <ObjectSomeValuesFrom> <ObjectProperty IRI="#hasIntensity"/> <Class IRI="#Normal"/> </ObjectSomeValuesFrom> In a similar fashion, the isoppositeof property connects two contrasting emotions; for example, Joy isoppositeof Sadness. The <ObjectSomeValuesFrom> element defines the object value (in this case Class) of the property for that class. 5

5 <Class IRI="#Joy"/> <ObjectSomeValuesFrom> <ObjectProperty IRI="#isOppositeOf"/> <Class IRI="#Sadness"/> </ObjectSomeValuesFrom> The iscomposedof property describes which two basic emotions constitute a complex emotion; for example, Contempt is a complex emotion which comprises of Anger and Disgust. <Class IRI="#Contempt"/> <ObjectSomeValuesFrom> <ObjectProperty IRI="#isComposedOf"/> <ObjectIntersectionOf> <Class IRI="#Anger"/> <Class IRI="#Disgust"/> </ObjectIntersectionOf> </ObjectSomeValuesFrom> The next section discusses how the ontology was evaluated. Fig. 3. EmotiOn: The Proposed Ontology VI. EVALUATION For the evaluation of the ontology, we used different DL (Description Logic) queries in order to check the consistencies of EmotiOn. DL query is a form of expression that is used to query ontology according to the description logics [9]. It means two things; (i) an ontology correctly specifies the domain, that is, classes and their defined properties; and (ii) they are linked to each other via correct use of defined properties. In simple words, the ontology model should reflect real-world domain being considered. DL Query has close resemblance to English language. Several queries were formulated and tested on the ontology. Fig. 4 shows one such query and its result. The aim of this query was to determine which emotions have a Normal intensity level. It can be seen that the query correctly fetched all the 8 primary emotions from the ontology. As another example, the query in fig. 5 was checked. Here, the purpose was to retrieve the contrasting emotion. As it can be seen, the result for opposite emotion to Joy was correctly returned as Sadness, via isoppositeof property. In another example, a query was formulated to show which complex emotion is made up of the given primary class(es). Figs. 6 and 7 demonstrate the queries and their results. It can be seen that when both primary emotions were provided, the result was a particular complex emotion. However, if only one of the primary emotions was provided, both the possible complex emotions were returned. Fig. 4. DL Query and Result for Emotions with Normal Intensity Fig. 5. DL Query and Result for Opposite Emotion

6 Fig. 6. DL Query and Result for Complex emotion with both Basic emotions provided Fig. 7. DL Query and Result for Complex emotion with one Basic emotion provided VII. CONCLUSION AND FUTURE WORK The ontology EmotiOn is designed with the aim to create a comprehensive model for Plutchik s wheel of emotions. It is part of a bigger goal of performing emotion analysis using an ontological approach. Automatic emotion analysis is valuable in different fields. It helps gain better insight into human feelings, in order to provide them better services, resolve conflicts, and predict behavior and trends. The ontology was created in OWL-DL and evaluated through DL queries over HermiT reasoner. The results indicated that the ontology is completely functional and consistent. EmotiOn can be used for a wide number of applications for emotion analysis; especially those which are based on inputs in the form of text. In future, EmotiOn can be used for analyzing emotions in various application areas or domains, like intelligent learning, or team facilitation, etc. For this purpose, an analyzer can be developed to demonstrate working capabilities of EmotiOn. It is expected that EmotiOn can be integrated and used with any such existing tool with minimal efforts. However, there are not many applications available for this purpose at present. Thus, it can be established that there is a need for some analyzers for automatic detection and analysis of emotions. In such a case, EmotiOn will be used as an integral component of the application. 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