The Hourglass of Emotions

Wang, Zhaoxia; Livingstone, Andrew; Hussain, Amir

doi:10.1007/978-3-642-34584-5_11

Cited by 226 publications

(109 citation statements)

References 31 publications

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“…As we mentioned before, in this work irony is considered as an umbrella term that covers sarcasm. Both IDM and emotIDM were designed to identify (SN) is a semantic resource where each concept is associated with the four dimensions of the Cambria's hourglass of emotions model [Cambria et al 2012]: Pleasantness, Attention, Sensitivity and Aptitude.…”

Section: Methodsmentioning

confidence: 99%

“…We will also consider such measure in our study (SN Formula). [Cambria et al 2012] and reinterprets the Plutchik's model by organizing primary emotions around four independent but concomitant dimensions (PleasantnessAttention-Sensitivity-Aptitude). In Table VI the resources related to dimensional models used in emotIDM are summarized.…”

Section: Sn Pol Sn Formulamentioning

confidence: 99%

“…A value of polarity is provided directly by the resource for each word (SN Pol). Each concept is associated with the four dimensions of the Cambria's hourglass of emotions model [Cambria et al 2012], and a polarity measure can be defined in terms of the four affective dimensions, according to the formula in [Cambria et al 2012]. We will also consider such measure in our study (SN Formula).…”

Section: Sn Pol Sn Formulamentioning

confidence: 99%

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Irony Detection in Twitter

Farías

Patti

Rosso

2016

ACM Trans. Internet Technol.

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Irony has been proven to be pervasive in social media, posing a challenge to sentiment analysis systems. It is a creative linguistic phenomenon where affect-related aspects play a key role. In this work, we address the problem of detecting irony in tweets, casting it as a classification problem. We propose a novel model which explores the use of affective features based on a wide range of lexical resources available for English, reflecting different facets of affect. Classification experiments over different corpora show that affective information helps in distinguishing among ironic and non-ironic tweets. Our model outperforms the stateof-the-art in almost all cases.

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Section: Methodsmentioning

confidence: 99%

Section: Sn Pol Sn Formulamentioning

confidence: 99%

Section: Sn Pol Sn Formulamentioning

confidence: 99%

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Irony Detection in Twitter

Farías

Patti

Rosso

2016

ACM Trans. Internet Technol.

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“…The research on human emotions dates back to ancient times [6]. One of the first categorizations of emotional states was made by Cicero, who separated them into four categories: fear, pain, lust, and pleasure.…”

Section: Emotional Modelingmentioning

confidence: 99%

“…However, they have some significant limitations, as they are largely based on the verbal representation of affects. In particular, they do not allow the definition of compound emotions, and they do not consider the situation where several emotions are being experienced at the same time [6].…”

Section: Emotional Modelingmentioning

confidence: 99%

A Blackboard System for Generating Poetry

Misztal-Radecka

Indurkhya

2016

csci

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We present a system to generate poems based on the information extracted from input text such as blog posts. Our design uses the blackboard architecture, in which independent specialized modules cooperate during the generation process by sharing a common workspace known as the blackboard. Each module is responsible for a particular task while generating poetry. Our implementation incorporates modules that retrieve information from the input text, generate new ideas, or select the best partial solutions. These distinct modules (experts) are implemented as diverse computational units that make use of lexical resources, grammar models, sentiment-analyzing tools, and languageprocessing algorithms. A control module is responsible for scheduling actions on the blackboard. We argue that the blackboard architecture is a promising way of simulating creative processes because of its flexibility and compliance with the Global Workspace Theory of mind. The main contribution of this work is the design and prototype implementation of an extensible platform for a poetry-generating system that may be further extended by incorporating new experts as well as some existing poetrygenerating systems as parts of the blackboard architecture. We claim that this design provides a powerful tool for combining many of the existing efforts in the domain of automatic poetry generation.

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Bibliography

2017

Natural Language Processing and Computational Linguistics 2

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The Hourglass of Emotions

Cited by 226 publications

References 31 publications

Irony Detection in Twitter

Irony Detection in Twitter

A Blackboard System for Generating Poetry

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