2016 49th Hawaii International Conference on System Sciences
Measuring Emotion Bifurcation Points for Individuals in Social Media Jiandong Zhou*#, Yanping Zhao*, Huaping Zhang& and Tianming Wang$ School of Management and Economics, Beijing Institute of Technology, Beijing 100081, P.R.China & School of Computer, Beijing Institute of Technology, Beijing 100081, P.R.China # School of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, P.R.China $ School of Mathematical Sciences, Dalian Institute of Technology, Dalian, 116024, P.R.China
[email protected], {zhaoyp, kevinzhang}@bit.edu.cn,
[email protected] *
services [13] and even stock returns [40] via text processing techniques. Generally, an affective event (also called an emotional event) is the event that is likely to generate emotional responses to the external environment, such as a job promotion, a meeting notification, or a demanding coworker [50]. These situations can be further interpreted by the affective event theory (AET) [46] that people's reactions such as opinion expression and decision-making behaviour, are heavily dominated by the mood level on the event just happened and the ability of emotion perception. Emotion, as one type of affect, has the characteristics of having a clear trigger and a short but more intense effect [10], which is a subjective feeling, triggered by a stimulus such as an event, an object or certain piece of information. Once the stimulus conditions, the stimulus itself or the supporting cognition, perceptions or other triggers are no longer active, emotion will disappear. What can not be ignored is that emotion can be highly contagious [14]. In social media environment, individual behavior is more than the outcome of rational decision making. Emotions are a major factor in providing valuable implicit or explicit information for making fast and advantageous decisions [2]. The emotions and opinions tweeted by some social celebrities have consciously/ unconsciously exerting good or bad influences on the choices and determinations of the followers. Gauging the emotion perception ability of individuals especially the figures is important for both opinion governance and online economic activities. The mechanism of the online individual's reactions to emotional events should be revealed, and the visualization of their ability of emotion perception worths conducting effective investigations. Several studies concentrated on analysing individual difference of emotion expression from the perspective of human personality based upon large-scale corpus consisting of the massive public posts on social networks [45]. Many works have investigated the relationship between written texts and personality of individual [17] [29] [40]. However, there are still challenges, for example, the changeful expression
Abstract Social media has been a new platform for emotion expression of individuals or groups in recent years. Millions of textual messages are constantly being generated. People with different emotion perceptions have different reactions to the same emotional event occurring in real life. However, it is hard to measure individual's emotion perception ability in both real world and social networks. This paper deals with online individual's emotion in view of complex system theory, and explores the emotion expression mechanism behind tweets. An concept of emotion bifurcation point is defined to denote the emotion perception ability and a methodological framework is proposed to measure it. Under the fundamental integration of the recognized Chinese emotion dictionaries (25,651 words included in total after reconciliation), new-born emotion words (458 in total) trained from a Sina-weibo corpus with 17 million tweets and commonly used emoticons (298 in total) as full-scale as possible, an emotion element ontology is constructed. Experimental evaluation on several certificated figures on Sina-weibo are implemented and the obtained results illustrate the reliability and validity of the proposed method.
1. Introduction A rich digital world has been created with the proliferation of various online social platforms such as Facebook, Twitter, Sina-weibo and other kinds of communication forums [39], where there are necessary and convenient accesses for people to express themselves emotionally and share information freely. The most potentially valuable part of these platforms are the constantly increasing data source of user-generated short messages containing opinions and sentiments regarding almost any subject or event in real life [34], which can be employed to infer people's emotion orientation [9], opinion trends [35], political election [48], ruminating behavior of depression [30], customer 1530-1605/16 $31.00 © 2016 IEEE DOI 10.1109/HICSS.2016.246
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behaviours in social media and the difficulty in formulating a model for measuring and visualizing of the hypostatic and emotion individuals’ sensitivity. In this paper, we first introduce the bifurcation model of affect structure (BMAS) [22] inspired by the bifurcation theory [25] into social media settings, and deal with individual's emotion as an adaptive complex system. Then we propose a novel methodological framework to interpret and visualize the nature of individuals' emotion bifurcation points in social networks environment. The remainder of this paper is organized as follows: in Section 2, we review some basic conceptions of emotion structure theories and the concepts of mining emotion enclosed through textual data on the Internet. In Section 3, we first construct an emotion element ontology and then propose a quantitative methodological framework to measure the emotion bifurcation points of individuals in social media environment. In Section 4, experiments with several real cases are presented to illustrate the effectiveness and convincingness of the proposed idea. Finally, Section 5 discusses our research, gives conclusions and suggests some directions for future studies.
of the people by extracting emotion-expressive textual pattens from unstructured archives (e.g., reports, logs, short messages). In terms of the processing granularity of the text, emotion-oriented mining can be divided into several levels such as word-level [26], sentence level [43], document level [23] as well as concept level [6]. By the emotional subjective information classification in text emotion-oriented mining field, there are several perspectives based on the aforementioned emotion structure theories such as positive/negative [24], pleasure-arousal-dominance [8] and so forth. Methods applied for emotion analysis generally include semantics-based approaches [44] and machine learning algorithms [36]. In addition, ontology as a formal specification of a shared conceptualization [12], has become one of the pillars of the semantic web designing for the purpose of enabling knowledge sharing and reuse. Ontology-driven sentiment analysis in the social semantic web (e.g., [3] [5] [18]) has become another promising direction. As the ways of emotion expression diversifies in social media such as newborn expressions, emoticons, it is necessary to extend the semantic ontology via integrating more emotion elements enclosed in messages posted online constantly. In this paper, an emotion element ontology is constructed on the integrating emotion words provided by dictionaries, newborn expressions extracted from large scale contents tweeted in social media and commonly used emoticons. Emotion perception, which is a critical component in social interactions, refers to the capacities and abilities of recognizing, identifying and expressing emotions. In human beings, there are three main kinds of emotional expressions [19]: (1) expressive and evaluative language, (2) physiologic changes mediated by the somatic and autonomic systems, and (3) behavioural interactions. The final emotional expression after emotion perception results from the individuals' subjective experiences and interpretations about the physical changes of others and environmental changes at the time the emotional event occurs [50]. It has been evaluated that there exists an emotion perception threshold for individual differences in emotional sensitivity [27]. Aiming at revealing the connections between emotions and the intensity of emotional events, and reconciling the theoretical discrepancies among the aforementioned emotion structure theories, the mentioned BMAS focused on exploring how emotion reacts to emotional events through first negative and positive feedback loops, and then self-organizing oscillation and transformations between four states: equilibrium emotion, discrete positive and negative emotion in near-equilibrium state, chaotic emotion and slow emotion reaction in dead state. Human emotion as a dissipative system is able to maintain its equilibrium by the interaction and balance through the positive and
2. Related work In recent two decades affective computing [37] has attracted many scholars focusing on computational social science [21], which is a new discipline that aims at using large archives of nationalistically-created behavioral data (e.g., emails, tweets) to answer social science questions, mainly including behavioural analysis [42], semantic web mining [15] and other interesting fields. Main emotion structure theories in psychology category includes discrete emotion theory [20], circumplex emotion theory [38], positive affect and negative affect theory [49] and others, including vector model [4], pleasure-arousal-dominance model [28], etc. Especially, positive affect and negative affect theory (PANA) has parsimony psychometric properties, PANA has come to be regarded as representative of the fundamental emotion structure [50] and be broadly employed in many research domains [11]. Based on the explorations on modeling human emotion, researchers so far have conducted many academic studies and developed practical applications in areas of emotionoriented computational social science in recent years. Aiming at extracting and leveraging meaningful information from the bulk of online subjective messages by social media users, emotion-oriented mining as one of the emerging automatic techniques has been a hotspot [47]. So far, there are numbers of excellent works by means of computer analytics and processing approaches to understand the intentions and behaviors
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negative feedback loops and thus determine the subsequent emotional states, which heavily depends upon the individual's emotion bifurcation point. However, although BMAS is superior to the extant models in revealing the connections between emotions and the intensity of emotional events in organizational settings, it waits to be extended for further research into revealing the different emotion perception ability of individuals or groups in social media, which is what this paper will account for. In this study, the introduced concept of emotion bifurcation point is to measure the level of emotion perception of online individuals.
we consider to construct an emotion element ontology which has three sources of emotion element (common emotion words, newborn emotion idioms and popularused emoticons in social media). Each emotion element has attributes of polarity (positive or negative) and intensity value. For the sake of convenient description and easy understanding, we denote the ontology as Ω = {( , , ), ( , , ), … , ( , , )} , where is the ith emotion element, is the polarity measure of and is the intensity value of . It is clear that is positive if > 0 and negative otherwise.
3. Methodology
Considering to encompass the commonly used emotion words as many as possible, several Chinese dictionaries are integrated by employing the Emotion Word Ontology (EWO) by Xu et al. [31] containing 27,466 emotion words stored in Excel, Hownet Sentiment Dictionary (HSD) (http://keenage.com/) containing 8,945 words, National Taiwan University Sentiment Dictionary (NTUSD) (http://nlg18.csie. ntu.edu.tw:8080/opinion/index.html) containing 2,812 positive words and 8,276 negative words, and the Chinese Emotion Word Ontology (CEWO) [51] which has under 5,500 verb predicates covering 113 different categories. We reconcile the polarity discrepancy of all emotion words from the dictionaries and ontologies by traversal query approach, and manually correct the polarity of those having obvious ambiguities by reverse order traversal approach and 7-point scoring method identified by experts. Also, the continuously generating novel emotional words and phrases in social media are frequently used in tweets by more and more individuals. Ignoring these newborn emotional expressions when conducting the research may cause large deviation from the expected results. We build a set of newborn emotion elements with 465 words and phrases by training on a large-scale social network corpus consisting of 1,700 million tweets from Sina-weibo using ICTCLAS (Institute of Computing Technology, Chinese Lexical Analysis System) (http://ictclas.nlpir.org /), which has obviously accuracy and speed in recognizing Chinese new word. Variety of emoticons with different meanings are also being used by almost every individual for vivid emotion expression of subjects or events in social media [16]. Emoticons are such important that can not be ignored. We rank the most frequently posted emoticons statistically and select the first 298 of them to build an emoticon element set for further integration. Therefore, the emotion words, newborn emotion expressions and the emoticons can be uniformly called by emotion elements.
3.1.1 Emotion element integration
In this section, we employ the concept of emotion bifurcation points to describe individual's different abilities of emotion perception for emotional events occurring in real life, and propose a novel methodological framework to detect and visualize their emotion bifurcation points in social media, where the emotion is recognized as an adaptive complex system in view of complex theory. The illustration on the methodology structure is shown by Figure 1.
Figure 1. Emotion perception and prediction framework for individuals in social media
3.1. Emotion element ontology In computer sciences, an ontology is a concept that specifies the world in terms of a set of types, relationships and properties [12]. For further conforming the emotion bifurcation points of individual,
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3.1.2 Emotion element ontology construction
After the integration of emotion elements and each element corresponds to one identified polarity tag, we need compute the intensity value to construct a complete emotion element ontology. The main procedures are summarized as follows. Corpus Collection Firstly, we form a corpus from four resources: (1) product review dataset from Jingdong Online Shopping1 (16MB, 4,000 positive comments and 4,000 negative comments); (2) Review dataset of 700 TV plays from Douban2(65MB); (3) Catering industry review data from Public Comment 3 ; (4) Large-scale corpus from Sina-weibo4(1,700 million tweets, 3.59GB). All the aforementioned four corpora are original and formed into one corpus without any manual pre-tagging. Denote the corpus as Θ to facilitate the description of text pretreatment work. Chinese Segmentation The API for Java of ICTCLAS is called to finish word segmentation work of Θ. Word Frequency Statistics We program in Java language and output the file of word frequency statistics as Excel and rank them in decreasing order of frequency. Remove Redundant Words The generic stop words (i.e., “the”, “a/an”, “and”, etc.) are relatively “safe” to remove and the removal rarely causes a significant loss in ontology construction effectiveness. Since stop words are commonly used and thus have a high frequency, we remove them by word frequency statistics. Intensity Calculation Denote ∗ as a set of emotion words fromΘ, which can further be separated to ∗ and ∗ for positive words set and negative words set, respectively. Each word from ∗ and ∗ can be segmented into certain number of characters, which can be marked by W = { … } . Note that every character from a emotion word has the same polarity of the word. We first compute the conditional probability distribution of each character by ( ∗ , ) (1) P( | ∗ ) = ∗ ( )
where is the ith character of W ; ( ∗ , ) is the probability when the character of W belong to ∗ in Θ; ( ∗ ) is the probability when all the characters from ∗ inΘ. We compute ( ∗ , ) and ( ∗ ) in a frequency statistics way as ( ∗ , ) ( ∗ , ) = (2) P( | ∗ ) = ∗ ∗ ) ( )
Then we compute the probability distribution of candidate emotion word by (" ∗ )# (3) P( ∗ ) = |%| ∑&' (" )
where * = 1/|Θ|, and |Θ| is the total number of words in Θ; ( ∗ ) is the occurrence frequency of candidate ) emotion word ∗ from Θ; ∑|+| - ( is the sum of the frequencies of all the words from Θ. To differentiate whether ∗ is an emotion word and obtain its polarity and intensity, we define ω(∗ | ∗ ) as the emotion weight of candidate word ∗ and compute it by ω(∗ | ∗ ) = α ∑ - 345( ∗ | ∗ ) + 7345(∗ ) (4) where ∗ is the ith character of ∗ ; α and 7 are parameters for computation adjustment and are set as α = 7 = 1/2; ( ∗ | ∗ ) and (∗ ) can be calculated by Eqs. (2) and (3), respectively. Note that S ∗ denotes on of the two part: ∗ and ∗ , that is, each candidate emotion word not only has a positive emotion weight but also has a negative emotion weight, denoted as ω(∗ | ∗ ) and ω(∗ | ∗ ) , respectively. Denote (∗ ) as the intensity value of ∗ , which is actually the difference of the two weights. Then we have (5) (∗ ) = ω(∗ | ∗ ) − ω(∗ | ∗ ) After the above-mentioned procedures, we obtain the intensity values of all the emotion elements. In addition, since the number of newborn emotion expressions and emoticons is not much large, and it is impossible to obtain the polarity and intensity value by modelling and analysing by computer, we manually annotate the polarity and intensity value of them by scoring method provided by experts. Thus, the emotion element ontology is constructed. Since the newborn emotion expressions and emoticons used on social media is user-preferred and time-dependent. Therefore, the ontology with rules should be updated over time, which thus can be shared with other ontologies. Because the constructed ontology is based on several large-scale corpora, it is consistent with the rationality of big data statistics and analysis, which is important for the subsequent work for determination of individual's emotion bifurcation point.
3.2. Emotion element ontology
(
where is a small enough positive constant to avoid the case that ( ∗ , ) = 0 if ( ∗ , ) = 0, which will cause trouble for the following analysis work. Here we assign δ = 1/! , where N is the total number of characters in ∗ .
Inspired by the bifurcation model proposed by May [25] (see Figure 2) and its associated terminology (dissipative system, positive and negative feedback, self-organizing, nonlinearity, and sensitive dependence on initial conditions), BMAS intended to elucidate the mechanisms through emotion occurs from perspective
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http://www.jd.com/ http://movie.douban.com/review/best/
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http://www.dianping.com/ http://open.weibo.com/
of complex adaptive system and reveal the relationships between emotions (see Figure 3), which considered human emotion as a dissipative system that exchanges energy and information with the external environment, that is, the emotional system reacts to environmental stimuli and presents as the concept of bifurcation corresponding with May's theory [25].
of mild anger as distinct from sadness or happiness as from surprise. Chaotic State Consistent with May's bifurcation theory, as $ r $ increases, the number of oscillation nodes correspondingly increases from two to four, to eight, or more. The emotional system becomes progressively more unstable and will requires more and more energy to maintain at its state. Chaotic state (3.57 < r ≤ 4) is such an emotional system where emotions change erratically without certain patterns. People in this state have so high emotion bifurcation point that they feel complicated to differentiate whether they should be angry, disappointed, anxious or mixed like hysterical. Chaotic emotion is an intensively activated and energy consuming experience where people will have inability to maintain it. Dead State Individual has dead emotion state with a much lower emotion bifurcation point (0 < r ≤ 1) . People in this emotion state will have a slow or even no reaction to the external event. Actually, in a social media environment, every kind of expression (e.g. tweeting) reflects individual's emotional system state at the time he/she perceives an emotional event in real life with certain level. In this paper, we focus on the detection and quantification of individual's emotion bifurcation point in social media via text analytics.
Figure 2. Illustration on bifurcation model
Figure 3. Bifurcation model of affective structure (BMAS)
There are four states: equilibrium state, nearequilibrium state, chaotic state and dead state. Due to the self organization characteristics of the emotional system, it can maintain its stability without external force by transiting from equilibrium to near-equilibrium to chaos and back again over time. By Figure 3, we see that a slight change in the emotion bifurcation point (r) will lead to different emotional consequences, which means that a stable state of emotional system is determined by r after an elicitation and interaction between the positive and negative feedback loops. Equilibrium State Taking the equilibrium state for an example, an emotional event is perceived at first, if r is low (1 < r ≤ 3) , the emotional system is at an equilibrium state. The individual feels that the proximal environment is constant and no matter how many such events occur around, he/she will consider that nothing is worthy expending energy or a reaction out of the ordinary and maintain a equilibrium emotion state within the range of r. The characteristics at the equilibrium state include some low-activated emotions such as calmness, ease, relaxation and comfort. Near-equilibrium State As discussed before, if r increases beyond the critical value of 3, the emotional system will becomes unstable and enters into a new state apart from equilibrium: near-equilibrium (3 < r ≤ 3.57). People in this state feel calm no more, but have feeling
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3.3. Quantitative emotion perception dynamics Dataset Since we aims to quantify individual's emotion bifurcation point (r) in social media environment, we first collect the tweets in time order to construct corpus individually via Sina-weibo API in Java programming language. For the sake of convenient description, we denote the corpus as U as follows U = { , B} = C : E
1 2F : B
where G is tweet posted at the time t (t = 1,2, … , T). Text pretreatment Main work of text pretreatment includes segment tweets into words, remove reluctant information unrelated to emotion (e.g. urls, stopping words and other signs). We employ the ICTCLAS again to finish these work. Moreover, we recognize the newborn emotion words or phrases and emoticons by Java programming. Then we obtain a new U only consisting of emotion elements as
U = {, B} = C :
E
1 2F : B
where HG = ( , , … , IJ ) is a set of elements from the tweet G (t = 1,2, … , T), KG is the number of elements in G . Note that we still can not conform whether the element is an emotion element at this stage.
Word matching from ontology In this procedure, we match and obtain the polarity and intensity of each emotion element with the emotion element ontology Ω constructed before by Java programming. Thus, U becomes
L 1 L U = {L, B} = C : 2 F : LE B LG = {< , , >, < , , >, … , < IMJ , IMJ , IMJ >}
where is the matching-output emotion ontology for the tweet G , is the emotion element, and (i = 1,2, … , λ′t ) are its corresponding polarity and intensity, respectively, λRQ is the number of matched emotion elements in G . Bifurcation point calculation First, in order to categorize emotion state into four: dead (0,1], equilibrium (1,3], near-equilibrium (3,3.7] and chaos (3.57,4], base on individual's different emotion bifurcation point. We correspondingly separate the intensity values of all the elements in Ω into four regions: (0,a), [a,b), [b,c), (c,d), where a = 0.25d, b = 0.75d, c =0.8925d and d = max|I(w)| is the maximum intensity value in the emotion element ontology. Then, we statistically match out the number of emotion elements of LG with its intensity value in the four intervals and denote them as V G , G , X G and YℎG , respectively. In order to figure out the most significant emotion state of tweet G , we calculate their ratios by [(V )G =
V \
, [( )G =
\
, [(X )G =
X \
, [(Yℎ)G =
Yℎ\
.
Let [G = ^_`{[(V )G , [( )G , [(X )G , [(Yℎ)G } represent the most significant emotion state of tweet G . If [G = [( )\ , then we think that the individual feels equilibrium when he/she posted tweet G . Likewise, if [G = [(X )\ , we then think that the individual feels near-equilibrium state (3 < r ≤ 3.57) when G is posted, the same reasoning with [G = [(V )\ , [G = [( )\ and [G = [(Yℎ)\ corresponding to the dead state (0 < r ≤ 1), equilibrium state (1 < r ≤ 3) and chaotic state (3.57 < r ≤ 4), respectively. Thus, we have the following approach to approximate the emotion bifurcation point of individuals in his/her tweet at time t
G =
⎧
1 + [( )\ ,
3 + 0.5[(X )\ ,
In order to evaluate the efficiency and reliability of the emotion element ontology construction algorithm aforementioned in Subsection 3.1, we conduct the following experiment. We randomly select the 900 emotion words in the integrated dictionary and denote it as TW=, where W is the emotion word vector, IC is the intensity value vector calculated by Eqs. (1)(2)(3)(4)(5) and IM is the intensity value vector annotated manually by method of human point rating. We compare the accuracy of IC and IM of emotional adjectives, nouns and verbs from the corpus from Jingdong, Douban and Public Comment, the estimating result is shown in Table 1, where the accuracy is the proportion of the true positives against all the positive results (both true positives and false positives). Table 1. Evaluation of the ontology construction algorithm
Then we rank TW according to the intensity value of positive and negative emotion word, respectively. The accuracy of the first 10, 50, 150, 250, 350 and 450 words in TW are summarized in Table 2, where Positive no. and Negative no. mean the number of positive words and negative words, respectively. From Table 2, we can see that the accuracy reaches around 91% with the increase of emotion words number, which indicates the effectiveness of the algorithm. Table 3. Accuracy with increasing emotion words number
Table 3. Emotion elements in the emotion element ontology
de [\ = [( )\ de [\ = [( )\ de [\ = [( )\ de [\ = [( )\ .
⎨3.57 + 0.43[(Yℎ)\ , [(V )\ , ⎩ Emotion perception dynamics We obtain a sequence of the emotion perception dynamics as f = ( , , … , E ), which shows individual's emotion perception ability of different emotional event in real life.
Intensity values of some emotion elements in the emotion element ontology is shown in Tables 3 and 4, where there are some newborn emotion elements: ᙍᇶ 䗮(a totally newly fabricate Chinese charactor strings explain about “despised”), ҏᱟ䞹Ҷ(it sounds like he is drunk but actually means “absurd”), ᣋ ᩜ ⹆ (it represents throwing a brick but actually means “angry”), 儈䙬Ṭ(it sounds like folk coarse, but actually “elegant”) and some popularly used emoticons.
4. Experimental evaluation 4.1. Quantitative emotion perception dynamics
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having emotion elements with low intensity value relates to a low emotion bifurcation point. By Figure 4, we statistically compare the number of Cui’s emotion bifurcation points falling into the four regions (equilibrium state, near equilibrium state, chaotic state and dead state) as 0, 12, 6 and 1, respectively. Cui’s average emotion bifurcation point is 2.78 by calculation which is at a high level in the region (1,3] corresponding to equilibrium state, which means that Cui’s common emotions mainly contain ease, relaxation and comfort, and mostly he view the proximal environment as constant.
Table 4. Emoticons in the emotion element ontology
4.2. Quantitative emotion perception dynamics In this procedure, we illustrate the quantification process of individual's emotion bifurcation point by some famous VIP users on Sina-weibo. First, we focus on Yongyuan Cui (http://weibo.com/cuiyongyuan) who is a well-known journalist of CCTV with 8,005,035 followers and has posted 4,479 tweets. We collect a corpus of 19 tweets posted from June, 2014 to July, 2014 on Cui's main page. For the sake of convenient description, we denote the corpus as U=, where S is the twitters vector and T it a corresponding posting time vector. Then we obtain Cui's emotion bifurcation points by the steps given in Subsection 3.3 as R=(1.52, 3.23, 2.33, 3.41, 1.86, 2.55, 2.94,1.83,2.55,1.91,3.56, 2.78, 3.51, 3.21, 3.88, 2.75, 2.84, 2.98, 3.25) corresponding to the tweet posted at different time. Cui’s emotion bifurcation points are shown in Figure 4, where the horizontal axis denotes time and the vertical axis denotes the emotion bifurcation points. The illustrative coordinate system is divided into four regions corresponding to the four emotion states: dead state (0,1), equilibrium state (1,3], near equilibrium state (3,3.57] and chaotic state (3.57,4).
Figure 5. Intensity values of emotion elements in Cui’s tweets Additionally, in Figure 4, the highest point ( g =3.88) falls into the region of chaotic state (3.57,4) and the points before the time node (i.e., h =3.51 and j =3.21) also maintain higher than the average level. We trawl back through Cui’s tweets posted before the time note 15 and find that there is a microblogging controversy occurring between Cui and another VIP blogger Fang towards an hot-button topic in China: the Transgenic event. See Figure 5, we statistically give the intensity values of emotion elements in Cui’s tweets (denoted by blue points inside circles). Within the tweets some emotion elements with high intensity values and even some fierce words are used such as ⍱≃ (rogue), ᙘ≄ (grievance), ⤇㹰 (dog blood, meaning “oh shit!”), ⻠ ⬧ (from the Beijing dialect, describing an fault finding or ill-disposed behavior), especially the tweet posted at the time note 15 with the highest intensity g =3.88 denoted by red solid circle in Figure 4, which corresponds to the red circle with a black arrow in Figure 5, where the bigger the circle is, the more emotion elements in the posted tweet. The intensities of the emotion elements in a tweet are marked as blue points inside the red circle. Similarly, following the same analysis methodology, we evaluate our model on another famous Chinese figure Yifei Liu (http://weibo.com/liuyifeiofficial) who is a famous actress with 38,503,477 followers and has posted 576 tweets. We collect a corpus of 19 tweets posted from June, 2014 to July, 2014 on Liu's main page of Sina-weibo. Then we measure her emotion bifurcation points shown in Figure 6.
Figure 4. Cui’s emotion bifurcation points We ask several network opinion experts to trawl through all the tweets posted by Cui and intuitively check whether it can fit well between the perception level reflected behind the posted tweet and the bifurcation point obtained via the proposed methodology. The experts give positive evaluation because mostly (93% by statistics) a tweet containing emotion elements with high intensity value relates to a high emotion bifurcation point in Figure 4, while a tweet
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6. Discussion and conclusion This study explores and makes it achievable how individual's emotion bifurcation point in social media environment be dynamically detected and visualized through the generating content on social media platform using text mining techniques. In this paper, the online emotion is dealt as an adaptive complex system from the perspective of complexity theory. A methodological framework is proposed to detect and visualize the emotion bifurcation points and emotion perception dynamics for individuals or groups in social media. The emotion words, newborn emotion expressions and emoticons are uniformly called as emotion elements in a dynamically-adding way. Under the fundamental integration work of emotion elements, an emotion element ontology is constructed instead of traditional manual annotation but by using our computational model. The ontology can be updated over time and be extended by integrating with other ontologies for bifurcation computation. Through experimental evaluation on several certificated users in Sina-weibo and verification by experts case by case, the framework is proved to be feasible and the results are convincing. The emotion elements used in tweets relate highly to individual's emotion perception ability, which differs from one to another in social media. Our study find that people with high emotion bifurcation point have high sensitivity to emotional events occurring in the surrounding environment and will mostly express themselves with emotion elements having high intensity values, while people with low emotion bifurcation point will mostly express themselves with low-intensity emotion elements. The study in this paper is useful for social emotion level or stage detection and early warning. Measuring the emotion bifurcation points of the figures with high number of followers will conduce to public emotion guidance of mass contingency events. In future studies, we will employ some machine learning algorithms to enhance the construction of the emotion element ontology in a field-oriented way for the intelligent applications of semantic web and explore more about decoding the dynamically changing emotion bifurcation points of individuals in social media. Additionally, study on the relationship between the level of figure's emotion bifurcation point and the emotion states of his/her followers is another interesting direction.
Figure 6. Liu’s emotion bifurcation points By Figure 6, we statistically count the number of Liu's emotion bifurcation points falling into the four regions as 0, 16, 3 and 0 , respectively, and Liu's average emotion bifurcation point is 2.40 by calculation which is lower than Cui’s. Trawling through Liu's tweets we find that she mostly use some emotion elements with low intensity values to express herself, e.g., 俘 (sweet), ᵏᖵ (expect), Ỗᜣ (dream), ⧽ᜌ (cherish).
Figure 7. Comparison between multi-individuals’ significant emotion bifurcation points Moreover, since the figures have different emotion bifurcation points on the same affect event, we define the average emotion perception level as the individual's significant emotion bifurcation point. We select Manan Si (http://weibo.com/simanan), Zhouzi Fang (http://weibo.com/fangzhouzi) and Na Xie (http://weibo.com/xiena) as another three individuals with 863,444, 2,820,000 and 71,952,445 followers, respectively. Figure 7 shows the significant emotion bifurcation points of the four individuals, corresponding to 3.12, 2.55, 2.89, 3.59 and 2.71, respectively. Fang’s point locates the highest level, which is due to the fact that Fang’s twitters contain many emotional elements with high intensity value. Conversely, Liu’s level of significant emotion perception 2.55 and Xie’s level of significant emotion perception 2.71 imply that Liu is not less emotion-sensitive to the event occurring in real life than Si, Cui and Fang. The higher the point locates, the more sensitive the individual will be and the higher probability he/she will express himself/herself with emotion elements having high intensity values in social platforms.
7. Acknowledgements This study is supported by the National 973 Basic Research Program of China (Grant No. 2013CB329606) and the National Natural Science Foundation of China (Grant No. 61272362).
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[14] Hatfield, E., Cacioppo, J. T., and Rapson, R. L., Emotional contagion. Cambridge university press, 1994.
8. References [1] Aisopos, F., Papadakis, G., Tserpes, K., and Varvarigou, T. (2012, June), “Content vs. context for sentiment analysis: a comparative analysis over microblogs”, in Proceedings of the 23rd ACM conference on Hypertext and social media. ACM, 2012, pp. 187-196.
[15] Hsu, P. L., Hsieh, H. S., Liang, J. H., and Chen, Y. S., “Mining various semantic relationships from unstructured user-generated web data”, Web Semantics: Science, Services and Agents on the World Wide Web, vol. 31, pp. 27-38, 2015.
[2] Bechara, A., and Damasio, A. R., “The somatic marker hypothesis: A neural theory of economic decision”, Games and economic behavior, vol. 52, no. 2, pp. 336-372 2005.
[16] Hogenboom, A., Bal, D., Frasincar, F., Bal, M., de Jong, F., and Kaymak, U., “Exploiting emoticons in sentiment analysis”, in Proceedings of the 28th Annual ACM Symposium on Applied Computing. ACM, 2013, pp. 703-710.
[3] Baldoni, M., Baroglio, C., Patti, V., and Rena, P., “From tags to emotions: Ontology-driven sentiment analysis in the social semantic web”, Intelligenza Artificiale, vol. 6, no. 1, pp. 41-54, 2012.
[17] Golbeck, J., Robles, C., and Turner, K., “Predicting personality with social media”, in CHI'11 extended abstracts on human factors in computing systems. ACM, 2011, pp. 253262.
[4] Bradley, M. M., Greenwald, M. K., Petry, M. C., and Lang, P. J., “Remembering pictures: pleasure and arousal in memory”, Journal of experimental psychology: Learning, Memory, and Cognition, vol. 18, no. 2, pp. 379, 1992.
[18] Kontopoulos, E., Berberidis, C., Dergiades, T., and Bassiliades, N., “Ontology-based sentiment analysis of twitter posts”, Expert systems with applications, vol. 40, no. 10, pp. 4065-4074, 2013.
[5] Borth, D., Chen, T., Ji, R., and Chang, S. F., “Sentibank: large-scale ontology and classifiers for detecting sentiment and emotions in visual content”, in Proceedings of the 21st ACM international conference on Multimedia. ACM, 2013, pp. 459-460.
[19] Lang, P. J., “Anxiety: Toward a psychophysiological definition”, Psychiatric diagnosis: Exploration of biological predictors, pp. 365-389, 1978.
[6] Cambria, E., Schuller, B., Liu, B., Wang, H., and Havasi, C., “Knowledge-based approaches to concept-level sentiment analysis”, IEEE Intelligent Systems, vol. 2, pp. 12-14, 2013.
[20] Lazarus, R. S., “Progress on a cognitive-motivationalrelational theory of emotion”, American psychologist, vol. 46, no. 8, pp 819, 1991.
[7] Camras, L., Grow, G., and Ribordy, S., “Recognition of emotional expression by abused children”, Journal of Clinical Child Psychology, vol. 12, pp. 325-328, 1983.
[21] Lazer, D., Pentland, A. S., Adamic, L., Aral, S., Barabasi, A. L., Brewer, D., ... and Van Alstyne, M., “Life in the network: the coming age of computational social science”, Science (New York, NY), vol. 323, no. 5915, pp 721, 2009.
[8] Cao, H. T., Li, M. C., Jiao, D., and Feng, B., “A Method of Micro-Blog Sentiment Analysis Based on PAD Model”, Advanced Materials Research, vol. 659, pp. 186-190, 2013.
[22] Li, Y., Ashkanasy, N. M., and Ahlstrom, D., “Complexity theory and affect structure: A dynamic approach to modeling emotional changes in organizations”, Research on emotion in organizations, vol. 6, pp. 139-165, 2010.
[9] Feldman, R., “Techniques and applications for sentiment analysis”, Communications of the ACM, vol. 56, no. 4, pp. 8289, 2013.
[23] Li, S., Zhang, H., Xu, W., Chen, G., and Guo, J., “Exploiting combined multi-level model for document sentiment analysis”, in Pattern Recognition (ICPR), 2010 20th International Conference on. IEEE, 2010, pp. 4141-4144.
[10] Frijda, N. H., “Varieties of affect: Emotions and episodes, moods, and sentiments”, The nature of emotions: Fundamental questions, pp. 197-202, 1994. [11] George, J.M., “State or trait: Effects of positive mood on prosocial behaviors at work”, Journal of Applied Psychology, vol. 76, pp. 299-307, 1991.
[24] Lin, Y., Zhang, J., Wang, X., and Zhou, A., “An information theoretic approach to sentiment polarity classification”, in Proceedings of the 2nd Joint WICOW/ AIRWeb Workshop on Web Quality. ACM, 2012, pp. 35-40.
[12] Gruber, T. R. (1993), “A translation approach to portable ontology specifications”, Knowledge acquisition, vol. 5, no. 2, pp. 199-220.
[25] May, R. M., “Simple mathematical models with very complicated dynamics”, Nature, vol. 261, no. 5560, pp. 459467, 1976.
[13] Gunarathne, P., Rui, H., and Seidmann, A., “Customer Service on Social Media: The Effect of Customer Popularity and Sentiment on Airline Response”, in System Sciences (HICSS), 2015 48th Hawaii International Conference on. IEEE, 2015, pp. 3288-3297.
[26] Maas, A. L., Daly, R. E., Pham, P. T., Huang, D., Ng, A. Y., and Potts, C., “Learning word vectors for sentiment analysis”, in Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, vol. 1, pp. 142-150, 2011.
1957
[27] Martin, R. A., Berry, G. E., Dobranski, T., Horne, M., and Dodgson, P. G., “Emotion perception threshold: individual differences in emotional sensitivity”, Journal of Research in Personality, vol. 30, no. 2, pp. 290-305, 1996.
[40] Sul, H. K., Dennis, A. R., and Yuan, L. I. (2014, January), “Trading on twitter: The financial information content of emotion in social media”, in System Sciences (HICSS), 2014 47th Hawaii International Conference on. IEEE, pp. 806-815.
[28] Mehrabian, A., and Russell, J. A., An approach to environmental psychology. the MIT Press, 1974.
[41] Staiano, J., Lepri, B., Aharony, N., Pianesi, F., Sebe, N., and Pentland, A., “Friends don't lie: inferring personality traits from social network structure”, in Proceedings of the 2012 ACM conference on ubiquitous computing. ACM, 2012, pp. 321-330.
[29] Minamikawa, A., and Yokoyama, H., “Blog tells what kind of personality you have: egogram estimation from Japanese weblog”, in Proceedings of the ACM 2011 conference on Computer supported cooperative work. ACM, 2011, pp. 217-220.
[42] Stieglitz, S., and Dang-Xuan, L., Political communication and influence through microblogging-An empirical analysis of sentiment in Twitter messages and retweet behavior, in System Science (HICSS), 2012 45th Hawaii International Conference on. IEEE, 2012, pp. 3500-3509.
[30] Nambisan, P., Luo, Z., Kapoor, A., Patrick, T. B., and Cisler, R., “Social Media, Big Data, and Public Health Informatics: Ruminating Behavior of Depression Revealed through Twitter”, in System Sciences (HICSS), 2015 48th Hawaii International Conference on. IEEE, 2015, pp. 29062913.
[43] Täckström, O., and McDonald, R., “Semi-supervised latent variable models for sentence-level sentiment analysis”, in Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers, vol. 2, pp. 569-574, 2011.
[31] Xu, L., Lin, H., Pan, Y., Ren, H., and Chen, J., “Constructing the affective lexicon ontology”, Journal of the China Society for Scientific and Technical Information, vol. 27, no. 2, pp. 180-185, 2008.
[44] Taboada, M., Brooke, J., Tofiloski, M., Voll, K., and Stede, M., “Lexicon-based methods for sentiment analysis”, Computational linguistics, vol. 37, no. 2, pp. 267-307, 2011.
[32] McCown, W., Johnson, J., and Austin, S., “Inability of delinquents to decode facial affects”, Journal of Social Behavior and Personality, vol. 1, pp. 89-96, 1986.
[45] Thelwall, M., Wilkinson, D., and Uppal, S., “Data mining emotion in social network communication: Gender differences in MySpace”, Journal of the American Society for Information Science and Technology, vol. 61, no. 1, pp. 190-199, 2010.
[33] McCown, W. G., Johnson, J., Silverman, N., and Austin, S., “Social interest and recognition of facial affects of emotion”, Individual Psychology: Journal of Adlerian Theory, Research & Practice, 1988.
[46] Thompson, E. R., and Phua, F. T., “A brief index of affective job satisfaction”, Group & Organization Management, vol. 37, no. 3, pp. 275-307. 2012.
[34] O'Connor, B., Balasubramanyan, R., Routledge, B. R., and Smith, N. A., “From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series”, ICWSM, vol 11, pp. 122-129, 2010.
[47] Tsytsarau, M., and Palpanas, T., “Survey on mining subjective data on the web”, Data Mining and Knowledge Discovery, vol. 24, no. 3, pp. 478-514, 2012.
[35] Parker, J., Wei, Y., Yates, A., Frieder, O., and Goharian, N., “A framework for detecting public health trends with twitter”, in Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. ACM, 2013, pp. 556-563.
[48] Tumitan, D., and Becker, K., “Sentiment-based features for predicting election polls: a case study on the brazilian scenario”, in Web Intelligence (WI) and Intelligent Agent Technologies (IAT), 2014 IEEE/WIC/ACM International Joint Conferences on. IEEE, 2014, vol. 2, pp. 126-133.
[36] Pennacchiotti, M., and Popescu, A. M., “A Machine Learning Approach to Twitter User Classification”, ICWSM, vol. 11, pp. 281-288, 2011.
[49] Watson, D., Clark, L. A., and Tellegen, A., “Development and validation of brief measures of positive and negative affect: the PANAS scales”, Journal of personality and social psychology, vol. 54, no. 6, pp 1063, 1988.
[37] Picard, R. W., Picard, R., Affective computing. Cambridge: MIT press, vol. 252, 1997.
[50] Weiss, H. M., and Cropanzano, R., Affective events theory: A theoretical discussion of the structure, causes and consequences of affective experiences at work, 1996.
[38] Russell, J. A., “A circumplex model of affect”, Journal of personality and social psychology, vol. 39, no. 6, pp 1161, 1980. [39] Shao, G., “Understanding the appeal of user-generated media: a uses and gratification perspective”, Internet Research, vol. 19, no. 1, pp. 7-25, 2009.
1958
[51] Yan, J., Bracewell, D. B., Ren, F., and Kuroiwa, S., “The Creation of a Chinese Emotion Ontology Based on HowNet”, Engineering Letters, vol. 16, no. 1, pp. 166-171, 2008.
