Spontaneous speech and opinion detection: mining call-centre transcripts

Clavel, Chloé; Adda, Gilles; Cailliau, Frederik; Garnier-Rizet, Martine; Cavet, Ariane; Chapuis, Géraldine; Courcinous, Sandrine; Danesi, Charlotte; Daquo, Anne-Laure; Deldossi, Myrtille; Guillemin-Lanne, Sylvie; Seizou, Marjorie; Suignard, Philippe

doi:10.1007/s10579-013-9224-5

Cited by 16 publications

(13 citation statements)

References 26 publications

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“…Text analysis has been demonstrated to be a powerful tool to identify emotional states (Clavel 2013). The detection of emotions such as anger, joy, sadness, fear, surprise, and disgust have also been addressed (Bellegarda 2010, Mohammad 2009).…”

Section: Emotion Recognition From Speech Signalsmentioning

confidence: 99%

Engagement with Artificial Intelligence through Natural Interaction Models

Feldman

Yalçın

DiPaola

2017

Electronic Workshops in Computing

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As Artificial Intelligence (AI) systems become more ubiquitous, what user experience design paradigms will be used by humans to impart their needs and intents to an AI system, in order to engage in a more social interaction? In our work, we look mainly at expression and creativity based systems, where the AI both attempts to model or understand/assist in processes of human expression and creativity. We therefore have designed and implemented a prototype system with more natural interaction modes for engagement with AI as well as other human computer interaction (HCI) where a more open natural communication stream is beneficial. Our proposed conversational agent system makes use of the affective signals from the gestural behaviour of the user and the semantic information from the speech input in order to generate a personalised, human-like conversation that is expressed in the visual and conversational output of the 3D virtual avatar system. We describe our system and two application spaces we are using it in -a care advisor / assistant for the elderly and an interactive creative assistant for uses to produce art forms.Artificial Intelligence. Natural user interfaces. Voice systems. Expression systems. ChatBots.

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Section: Emotion Recognition From Speech Signalsmentioning

confidence: 99%

Engagement with Artificial Intelligence through Natural Interaction Models

Feldman

Yalçın

DiPaola

2017

Electronic Workshops in Computing

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“…[73] refers to this definition of appraisal expression and its relation to the target and the source for the analysis of customer opinion in call-center transcripts. [66] proposes an @AM (ATtitude Analysis Model) modeling the previously described appraisal model and distinguishing affect, judgment and appreciation (see Section 2.1.1) on sentences extracted from personal stories.…”

Section: Opinion Mining Perspectivementioning

confidence: 99%

“…• Detection of the target of the user sentiment in: reviews [72], callcenters [73], personal stories [66], using for example Martin and White's appraisal model [36]. • Textual affect sensing [28], [70] and representation of affective knowledge in lexical resources [97], for example with the OCC appraisal model.…”

Section: Appraisal Approachesmentioning

confidence: 99%

“…Thus, the confidence score and the various hypotheses of the ASR outputs have to be taken into account. This issue is scarcely handled in the sentiment analysis/opinion mining community [73,18,161] but is already handled by other communities: for example, [162] investigated the effectiveness of using more ASR hypotheses (n-best lists) to extract lexical features for the automatic analysis of the level of interest in spoken dialogs.…”

Section: Asr Outputs and Spontaneous Speech Featuresmentioning

confidence: 99%

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Sentiment Analysis: From Opinion Mining to Human-Agent Interaction

Clavel

Callejas

2016

IEEE Trans. Affective Comput.

128

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The opinion mining and human-agent interaction communities are currently addressing sentiment analysis from different perspectives that comprise, on the one hand, disparate sentiment-related phenomena and computational representations, and on the other hand, different detection and dialog management methods. In this paper we identify and discuss the growing opportunities for cross-disciplinary work that may increase individual advances. Sentiment/opinion detection methods used in human-agent interaction are indeed rare and, when they are employed, they are not different from the ones used in opinion mining and consequently not designed for socio-affective interactions (timing constraint of the interaction, sentiment analysis as an input and an output of interaction strategies). To support our claims, we present a comparative state of the art which analyzes the sentiment-related phenomena and the sentiment detection methods used in both communities and makes an overview of the goals of socio-affective human-agent strategies. We propose then different possibilities for mutual benefit, specifying several research tracks and discussing the open questions and prospects. To show the feasibility of the general guidelines proposed we also approach them from a specific perspective by applying them to the case of the Greta embodied conversational agents platform and discuss the way they can be used to make a more significative sentiment analysis for human-agent interactions in two different use cases: job interviews and dialogs with museum visitors.Index Terms-Sentiment analysis, opinion mining, embodied conversational agents (ECAs), human-agent interaction, affect, emotion, socio-affective interaction ! • C. Clavel is with the Institut Mines-Télécom, Télécom ParisTech,

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“…MEWS provides the first public index into this data, providing faceted, full text search, with further interface developments that exploit the structure of parliamentary debates. For some debates, we can also browse the video recording of these debates using an automatic alignment and synchronization performed by Vecsys between the manual transcript and the video [3].…”

Section: Sources To Enrich Newsmentioning

confidence: 99%

Semantic dispatching of multimedia news with MEWS

Law-To¹,

Grefenstette²,

Landais³

2013

Proceedings of the 21st ACM International Conference on Multimedia

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Online news comes in rich multimedia form: video, audio, photos, in addition to traditional text. Recent advances in semanticallyrich text processing, in speech-to-text processing, and in image processing allows us to develop new ways of presenting and enriching news stories. Here we present MEWS, a Multimedia nEWS platform, which enriches news browsing according to media (text, images, and video) and to automatically detected type of news (music, general news, politics).

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Spontaneous speech and opinion detection: mining call-centre transcripts

Cited by 16 publications

References 26 publications

Engagement with Artificial Intelligence through Natural Interaction Models

Engagement with Artificial Intelligence through Natural Interaction Models

Sentiment Analysis: From Opinion Mining to Human-Agent Interaction

Semantic dispatching of multimedia news with MEWS

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