Automated Mark Up of Affective Information in English Texts

Francisco, Virginia; Gervás, Pablo

doi:10.1007/11846406_47

Cited by 26 publications

(26 citation statements)

References 5 publications

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“…Detecting emotions in text has a number of applications including tracking sentiment towards politicians, movies, and products (Pang and Lee, 2008), identifying what emotion a newspaper headline is trying to evoke (Bellegarda, 2010), developing more natural text-to-speech systems (Francisco and Gervás, 2006), detecting how people use emotion-bearing-words and metaphors to persuade and coerce others (for example, in propaganda) (Kǒvecses, 2003), tracking response to natural disasters (Mandel et al, 2012), and so on. With the rapid proliferation of microblogging, there is growing amount of emotion analysis research on newly available datasets of Twitter posts (Mandel et al, 2012;Purver and Battersby, 2012;Mohammad, 2012b).…”

Section: Introductionmentioning

confidence: 99%

Semantic Role Labeling of Emotions in Tweets

Mohammad¹,

Zhu²,

Martin³

2014

Proceedings of the 5th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

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Past work on emotion processing has focused solely on detecting emotions, and ignored questions such as 'who is feeling the emotion (the experiencer)?' and 'towards whom is the emotion directed (the stimulus)?'. We automatically compile a large dataset of tweets pertaining to the 2012 US presidential elections, and annotate it not only for emotion but also for the experiencer and the stimulus. We then develop a classifier for detecting emotion that obtains an accuracy of 56.84 on an eight-way classification task. Finally, we show how the stimulus identification task can also be framed as a classification task, obtaining an F-score of 58.30.

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Section: Introductionmentioning

confidence: 99%

Semantic Role Labeling of Emotions in Tweets

Mohammad¹,

Zhu²,

Martin³

2014

Proceedings of the 5th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

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“…There is less work on other emotions, for example, work by Pearl and Steyvers (2010) that focuses on politeness, rudeness, embarrassment, formality, persuasion, deception, confidence, and disbelief. Francisco andGervás (2006) marked sentences in fairy tales with tags for pleasantness, activation, and dominance, using lexicons of words associated with the three categories. Francisco andGervás (2006) marked sentences in fairy tales with tags for pleasantness, activation, and dominance, using lexicons of words associated with the three categories.…”

Section: Emotion Detectionmentioning

confidence: 99%

Using Hashtags to Capture Fine Emotion Categories from Tweets

Mohammad

Kiritchenko

2014

Computational Intelligence

303

108

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Detecting emotions in microblogs and social media posts has applications for industry, health, and security. Statistical, supervised automatic methods for emotion detection rely on text that is labeled for emotions, but such data are rare and available for only a handful of basic emotions. In this article, we show that emotion-word hashtags are good manual labels of emotions in tweets. We also propose a method to generate a large lexicon of word-emotion associations from this emotion-labeled tweet corpus. This is the first lexicon with real-valued word-emotion association scores. We begin with experiments for six basic emotions and show that the hashtag annotations are consistent and match with the annotations of trained judges. We also show how the extracted tweet corpus and word-emotion associations can be used to improve emotion classification accuracy in a different nontweet domain.Eminent psychologist Robert Plutchik had proposed that emotions have a relationship with personality traits. However, empirical experiments to establish this relationship have been stymied by the lack of comprehensive emotion resources. Because personality may be associated with any of the hundreds of emotions and because our hashtag approach scales easily to a large number of emotions, we extend our corpus by collecting tweets with hashtags pertaining to 585 fine emotions. Then, for the first time, we present experiments to show that fine emotion categories such as those of excitement, guilt, yearning, and admiration are useful in automatically detecting personality from text. Stream-of-consciousness essays and collections of Facebook posts marked with personality traits of the author are used as test sets.

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“…The perceived importance of affect for human–computer interaction has been repeatedly recognized, and there are many other examples of the strong interest in affective NLP (e.g. Bellegarda 2010; Boucouvalas 2002; Francisco and Gervás 2006a,b; Généreux and Evans 2006; Gupta, Gilbert, and Fabbrizio 2010; Holzman and Pottenger 2003; John, Boucouvalas, and Xu 2006; Karla and Karahalios 2005; Ma, Prendinger, and Ishizuka 2005; Mathieu 2005; Neviarouskaya, Prendinger, and Ishizuka 2010; Ries and Waibel 2001; Sauper, Haghighi, and Barzila 2011; Subasic and Huettner 2000).…”

Section: Considerations For Computational Approaches To Affect In mentioning

confidence: 99%

The Role of Affect in the Computational Modeling ofNaturalLanguage

Alm

2012

Language and Linguist. Compass

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Expressivity is an intrinsic component of natural language. This article follows the tradition of affective computing (Picard 1997) in using affect to refer to connected concepts such as emotion, mood, feelings, personality, attitude, polarity, and related subjective phenomena. The overview clarifies the relevance of affect for linguistics and computational linguistics, summarizes useful background, outlines helpful resources, and highlights important considerations for computational modeling of affect in language and affect‐related linguistic behaviors. In addition, the article sketches unsettled debates, topics, problems, and areas in need of further exploration.

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Automated Mark Up of Affective Information in English Texts

Cited by 26 publications

References 5 publications

Semantic Role Labeling of Emotions in Tweets

Semantic Role Labeling of Emotions in Tweets

Using Hashtags to Capture Fine Emotion Categories from Tweets

The Role of Affect in the Computational Modeling ofNaturalLanguage

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