jurafsky&martin_3rdEd_17 (1).pdf

Heres a simple such sentiment algorithm f x w st w 2

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, these can be used as well. Here’s a simple such sentiment algorithm: f + = X w s.t. w 2 positivelexicon q + w count ( w ) f - = X w s.t. w 2 negativelexicon q - w count ( w ) sentiment = 8 > > > < > > > : + if f + f - > l - if f - f + > l 0 otherwise. (18.11) If supervised training data is available, these counts computed from sentiment lexicons, sometimes weighted or normalized in various ways, can also be used as
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338 C HAPTER 18 L EXICONS FOR S ENTIMENT AND A FFECT E XTRACTION features in a classifier along with other lexical or non-lexical features. We return to such algorithms in Section 18.7 . 18.5 Emotion and other classes One of the most important affective classes is emotion , which Scherer (2000) defines emotion as a “relatively brief episode of response to the evaluation of an external or internal event as being of major significance”. Detecting emotion has the potential to improve a number of language processing tasks. Automatically detecting emotions in reviews or customer responses (anger, dissatisfaction, trust) could help businesses recognize specific problem areas or ones that are going well. Emotion recognition could help dialog systems like tutoring systems detect that a student was unhappy, bored, hesitant, confident, and so on. Emotion can play a role in medical informatics tasks like detecting depression or suicidal intent. Detecting emotions expressed toward characters in novels might play a role in understanding how different social groups were viewed by society at different times. There are two widely-held families of theories of emotion. In one family, emo- tions are viewed as fixed atomic units, limited in number, and from which others are generated, often called basic emotions ( Tomkins 1962 , Plutchik 1962 ). Per- basic emotions haps most well-known of this family of theories are the 6 emotions proposed by (Ekman, 1999) as a set of emotions that is likely to be universally present in all cultures: surprise, happiness, anger, fear, disgust, sadness . Another atomic theory is the (Plutchik, 1980) wheel of emotion, consisting of 8 basic emotions in four opposing pairs: joy–sadness , anger–fear , trust–disgust , and anticipation–surprise , together with the emotions derived from them, shown in Fig. 18.11 . Figure 18.11 Plutchik wheel of emotion.
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18.5 E MOTION AND OTHER CLASSES 339 The second class of emotion theories views emotion as a space in 2 or 3 di- mensions (Russell, 1980) . Most models include the two dimensions valence and arousal , and many add a third, dominance . These can be defined as: valence: the pleasantness of the stimulus arousal: the intensity of emotion provoked by the stimulus dominance: the degree of control exerted by the stimulus Practical lexicons have been built for both kinds of theories of emotion. 18.5.1 Lexicons for emotion and other affective states While semi-supervised algorithms are the norm in sentiment and polarity, the most common way to build emotional lexicons is to have humans label the words. This is most commonly done using crowdsourcing : breaking the task into small pieces crowdsourcing and distributing them to a large number of annotaters. Let’s take a look at one
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