Measuring emotional valence to understand the user's experience of software

Hazlett, Richard L.; Benedek, Joey

doi:10.1016/j.ijhcs.2006.11.005

Cited by 65 publications

(49 citation statements)

References 33 publications

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“…The need to quantify affect valence arises in numerous domains, ranging from medical or psychological studies of well-being to market research, advertising, usability and product evaluation [8]. Self-report is the current gold-standard measure of affect valence, where people are interviewed, asked to rate their feeling on a Likert scale or turn a dial to quantify valence.…”

Section: Introductionmentioning

confidence: 99%

Affect valence inference from facial action unit spectrograms

McDuff

Kaliouby

Picard

2010

2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops

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The face provides an important channel for communicating affect valence, the positive or negative emotional charge of an experience. This paper addresses the challenging pattern recognition problem of assigning affect valence labels (positive, negative or neutral) to facial action sequences obtained from unsegmented videos coded using the Facial Action Coding System (FACS). The data were obtained from viewers watching eight short movies with each second of video labeled with self-reported valence and hand coded using FACS. We identify the most frequently occurring Facial Actions and propose the usefulness of a Facial Action Unit Spectrogram. We compare both generative and discriminative classifiers on accuracy and computational complexity: Support Vector Machines, Hidden Markov Models, Conditional Random Fields and Latent-Dynamic Conditional Random Fields. We conduct three tests of generalization with each model. The results provide a first benchmark for classification of self-report valences from spontaneous expressions from a large group of people (n=42). Success is demonstrated for increasing levels of generalization and discriminative classifiers are shown to significantly outperform generative classifiers over this large data set. We discuss the challenges encountered in dealing with a naturalistic dataset with sparse observations and its implications on the results.

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Section: Introductionmentioning

confidence: 99%

Affect valence inference from facial action unit spectrograms

McDuff

Kaliouby

Picard

2010

2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops

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“…The paradigm adopts a MMORPG as the task for speakers to induce their emotion. The effect of a game to induce emotion was proved in earlier studies [20][21][22][23][24][25]. Emotion expressivity is the characteristic that vocal expression is influenced by induced emotion.…”

Section: Emotional Speech Collection Paradigmmentioning

confidence: 95%

“…To induce speaker's emotion, a Massively Multiplayer Online Role-Playing Game (MMORPG) is adopted with reference to the research on emotion and decision making [20][21][22] and emotion studies for entertainment computing [23][24][25], and the subject's emotional response to the game is successfully induced. As our analytical interest is focused on speech, our goal is not just to induce the speaker's emotion but also to record naturalistic expressive utterances containing spontaneous emotion evoked in a usual environment for speakers.…”

Section: Introductionmentioning

confidence: 99%

Naturalistic emotional speech collection paradigm with online game and its psychological and acoustical assessment

Arimoto

Kawatsu

Ohno

et al. 2012

Acoust. Sci. & Tech.

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For the purpose of constructing a naturalistic emotional speech database, a novel paradigm of collecting naturalistic emotional speech during a spontaneous Japanese dialog was proposed. The proposed paradigm was assessed by investigating whether the collected speech contains and conveys rich emotions psychologically and acoustically. To encourage speakers to experience and express their natural and vivid emotions, a Massively Multiplayer Online Role-Playing Game (MMORPG) was adopted as a task for speakers. They were asked to play the MMORPG together while discussing strategies to achieve their tasks through a voice chat system. The recording was performed for one hour per speaker. The total recording time was approximately 14 hours. The results of emotional labeling for the collected speech supported the validity of the paradigm showing higher interlabeler agreement than the chance levels. In addition, it was revealed that the paradigm is superior in the quantity of emotional speech to other paradigm by showing a significantly higher rate of labeling instances for our speech material (73%, 2 ð2Þ ¼ 27659:87, p < 0:001) than other speech materials. Finally, an acoustical analysis supported the validity of the paradigm, showing a significant difference between the nonemotional utterances and the emotional utterances (p < 0:05).

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“…There are a few studies on fusing affect recognition and usability evaluation [2][4] [12][13][14][15][16][17]. Most of them consider the usability of food or everyday items and evaluate the overall experience taking emotional factors into account.…”

Section: Related Workmentioning

confidence: 99%

“…The multimodal approach used in this study was able to provide a measure of the desirability of features, a measure of emotional tension and mental effort expended while performing tasks [17]. There are also some studies of games that utilise the channels used in emotion recognition [18][19] [20].…”

Section: Related Workmentioning

confidence: 99%

Limitations of Emotion Recognition in Software User Experience Evaluation Context

Landowska¹,

Miler²

2016

Annals of Computer Science and Information Systems

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Abstract-This paper concerns how an affective-behaviouralcognitive approach applies to the evaluation of the software user experience. Although it may seem that affect recognition solutions are accurate in determining the user experience, there are several challenges in practice. This paper aims to explore the limitations of the automatic affect recognition applied in the usability context as well as to propose a set of criteria to select input channels for affect recognition. The results are revealed via a semi-experiment based on the case study of an educational game. As a result, a number of concerns were identified, providing a list of pros and cons for affective computing methods applied in the usability testing context. The lessons learned might be interesting for both researchers that develop emotion recognition algorithms and for practitioners, who apply them to diverse areas.

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Measuring emotional valence to understand the user's experience of software

Cited by 65 publications

References 33 publications

Affect valence inference from facial action unit spectrograms

Affect valence inference from facial action unit spectrograms

Naturalistic emotional speech collection paradigm with online game and its psychological and acoustical assessment

Limitations of Emotion Recognition in Software User Experience Evaluation Context

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