Contextual affect analysis: a system for verification of emotion appropriateness supported with Contextual Valence Shifters

Ptaszyński, Michał; Dybała, Paweł; Shi, Wenhan; Rzepka, Rafał; Araki, Kenji

doi:10.1504/ijbm.2010.031793

Cited by 9 publications

(6 citation statements)

References 27 publications

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“…The number of examples, the number of words, and the number of vocabularies are shown in Table 4. There are many approaches to estimating emotion from sentences using linguistic features [34][35][36][37][38]. These approaches often use machine learning methods such as neural networks for training the emotion estimators [39,40].…”

Section: Neural Networkmentioning

confidence: 99%

Emotion Analysis and Dialogue Breakdown Detection in Dialogue of Chat Systems Based on Deep Neural Networks

et al. 2022

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In dialogues between robots or computers and humans, dialogue breakdown analysis is an important tool for achieving better chat dialogues. Conventional dialogue breakdown detection methods focus on semantic variance. Although these methods can detect dialogue breakdowns based on semantic gaps, they cannot always detect emotional breakdowns in dialogues. In chat dialogue systems, emotions are sometimes included in the utterances of the system when responding to the speaker. In this study, we detect emotions from utterances, analyze emotional changes, and use them as the dialogue breakdown feature. The proposed method estimates emotions by utterance unit and generates features by calculating the similarity of the emotions of the utterance and the emotions that have appeared in prior utterances. We employ deep neural networks using sentence distributed representation vectors as the feature. In an evaluation of experimental results, the proposed method achieved a higher dialogue breakdown detection rate when compared to the method using a sentence distributed representation vectors.

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Section: Neural Networkmentioning

confidence: 99%

Emotion Analysis and Dialogue Breakdown Detection in Dialogue of Chat Systems Based on Deep Neural Networks

et al. 2022

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“…Most commonly, the system was used to analyze user input in human-agent interaction [1,3,4,15,23,24,25,26]. In particular, the analysis of user input was utilized in decision making support about which conversation strategy to choose (normal conversation or joke) [1], and in an automatic evaluation method for dialog agents [3].…”

Section: Russell's 2-dimensional Model Of Affectmentioning

confidence: 99%

“…The system was most often evaluated on conversations, both between humans [1] and between human users and conversational agents [4,3,15,23,26]. In [1] Dybala et al showed that ML-Ask presents comparable answers to human annotators when annotating conversations between people of different age and status (in particular young students vs. middle-aged businessmen).…”

Section: Quality Controlmentioning

confidence: 99%

“…In other evaluations Ptaszynski et al showed that the system performs comparably to humans when annotating human-agent dialogs. This was evaluated using only ML-Ask [3,4], and ML-Ask confronted with the Webmining procedure [23,26]. Recently Ptaszynski et al added also emoticon analysis system CAO to this evaluation [35].…”

Section: Quality Controlmentioning

confidence: 99%

“…• Analysis of user input [1, 3, 4, 15] [23, 24, 25, 26] [35] • Decision making support [1,4] • Automatic evaluation [3] Verification of emotion appropriateness [23,24,25,26] [35]…”

Section: Applicationsmentioning

confidence: 99%

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