The Dictionary of Affect in Language

Whissell, Cynthia

doi:10.1016/b978-0-12-558704-4.50011-6

Cited by 353 publications

(212 citation statements)

References 24 publications

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“…(Whissell's Dictionary of Affect Language) [23] The DAL list contains 8742 words rated by people for their activation, evaluation and imagery.…”

Section: Anew Word List (Affective Norms For English Words) [3]mentioning

confidence: 99%

Ontology-Based Textual Emotion Detection

Haggag¹,

Fathy²,

El-Haggar³

2015

ijacsa

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Abstract-EmotionDetection from text is a very important area of natural language processing. This paper shows a new method for emotion detection from text which depends on ontology. This method is depending on ontology extraction from the input sentence by using a triplet extraction algorithm by the OpenNLP parser, then make an ontology matching with the ontology base that we created by similarity and word sense disambiguation. This ontology base consists of ontologies and the emotion label related to each one. We choose the emotion label of the sentence with the highest score of matching. If the extracted ontology doesn't match any ontology from the ontology base we use the keyword-based approach. This method doesn't depend only on keywords like previous approaches; it depends on the meaning of sentence words and the syntax and semantic analysis of the context.

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“…(Whissell's Dictionary of Affect Language) [23] The DAL list contains 8742 words rated by people for their activation, evaluation and imagery.…”

Section: Anew Word List (Affective Norms For English Words) [3]mentioning

confidence: 99%

Ontology-Based Textual Emotion Detection

Haggag¹,

Fathy²,

El-Haggar³

2015

ijacsa

View full text Add to dashboard Cite

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“…Activation is the degree of arousal associated with terms such as patient (at 3.3) representing a midpoint, surprised (over 6) representing high activation and bashful (around 2) representing low activation. Evaluation is the degree of pleasantness associated with the terms guilty (at 1.1) representing the negative extreme and delighted (at 6.4) representing the positive extreme [8]. In our experience, the estimation of evaluation through gross facial attributes is difficult (even intractable).…”

Section: Creating Profiles For Nonarchetypal Expressionsmentioning

confidence: 99%

“…Psychologists have examined a broader set of emotions [17], but very few of the studies provide results that can be exploited in computer graphics and machine vision fields. One of these studies carried out by Whissel [8], suggests that emotions are points in a space spanning a relatively small number of dimensions that seem to occupy two axes: activation and evaluation, as shown in Table 9.6. Activation is the degree of arousal associated with terms such as patient (at 3.3) representing a midpoint, surprised (over 6) representing high activation and bashful (around 2) representing low activation.…”

Section: Creating Profiles For Nonarchetypal Expressionsmentioning

confidence: 99%

“…Evaluation summarizes how positive or negative the subject feels; activation indicates how energetically he or she is disposed to act. The scheme is useful partly because research such as Whissel's [8] has provided coordinates corresponding to a wide range of emotions.…”

Section: Introductionmentioning

confidence: 99%

“…This trend may reflect the influence of work by Ekman [5], Friesen [6] and others, who proposed that the archetypal emotions correspond to distinct facial expressions that are supposed to be universally recognizable across cultures. However, psychological researchers working in different traditions [7][8][9] have investigated a broader variety of emotions. An extensive survey on emotion analysis can be found in Reference 10.…”

Section: Introductionmentioning

confidence: 99%

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Modeling the human factors of scholarly communities supported through the Internet and World Wide Web

Gaines

Chen

Shaw

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The Internet (the net) and World Wide Web (the web) have grown rapidly in the past decade and have come to play a major role in supporting discourse and publication in scholarly communities. The development and application of new services has been very rapid with little central planning, and, despite the widespread use, there is little information as yet on the human factors of the use of the net and web. In particular, models of the human factors of individuals interacting with workstations have to be extended to take into account the essential social aspects of computer-mediated discourse and publication. This article provides a framework for analyzing the utility, usability and likeability of net and web services, and illustrates its application to significant aspects of supporting scholarly communities. The utility of the net and web are measured in terms of the growth of usage, and the different services involved are distinguished in terms of their specific utilities. A layered protocol model is used to model discourse through the net, and is extended to encompass interaction in communities. An operational criterion for distinguishing different communities is defined in terms of the types of awareness that resource providers and users have of one another. A temporal model of discourse processes is developed that enables the spectrum of services ranging from real-time discourse to long-term publication to be analyzed in a unified framework. The dimensions of awareness and time are used to characterize and compare the full range of net services, and model their unification through the next generation of web browsers.

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The Dictionary of Affect in Language

Cited by 353 publications

References 24 publications

Ontology-Based Textual Emotion Detection

Ontology-Based Textual Emotion Detection

Emotion Recognition and Synthesis Based on MPEG‐4 FAPs

Modeling the human factors of scholarly communities supported through the Internet and World Wide Web

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