Learning Lexico-Functional Patterns for First-Person Affect

Reed, Lena; Wu, Jiaqi; Oraby, Shereen; Anand, Pranav; Walker, Marilyn A.

doi:10.18653/v1/p17-2022

Cited by 14 publications

(18 citation statements)

References 21 publications

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“…In these contexts, users might be less able to evaluate the results of algorithmic interpretations [34,46]. In addition, emotion is highly variable between individuals and previous research demonstrates difficulty in accurately predicting emotion from text [48,71]. One of the major challenges with intelligent systems is handling errors [50,51,60,81]; explaining algorithmic prediction in this difficult domain of emotional analytics allows us to better understand how users make sense of output that contains errors.…”

Section: Emotional Analyticsmentioning

confidence: 99%

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Springer

Whittaker

2020

ACM Trans. Interact. Intell. Syst.

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It is essential that users understand how algorithmic decisions are made, as we increasingly delegate important decisions to intelligent systems. Prior work has often taken a techno-centric approach, focusing on new computational techniques to support transparency. In contrast, this article employs empirical methods to better understand user reactions to transparent systems to motivate user-centric designs for transparent systems. We assess user reactions to transparency feedback in four studies of an emotional analytics system. In Study 1, users anticipated that a transparent system would perform better but unexpectedly retracted this evaluation after experience with the system. Study 2 offers an explanation for this paradox by showing that the benefits of transparency are context dependent. On the one hand, transparency can help users form a model of the underlying algorithm's operation. On the other hand, positive accuracy perceptions may be undermined when transparency reveals algorithmic errors. Study 3 explored real-time reactions to transparency. Results confirmed Study 2, in showing that users are both more likely to consult transparency information and to experience greater system insights when formulating a model of system operation. Study 4 used qualitative methods to explore real-time user reactions to motivate transparency design principles. Results again suggest that users may benefit from initially simplified feedback that hides potential system errors and assists users in building working heuristics about system operation. We use these findings to motivate new progressive disclosure principles for transparency in intelligent systems and discuss theoretical implications. CCS Concepts: • Human-centered computing → Ubiquitous and mobile computing design and evaluation methods; • Social and professional topics → Government technology policy; • Human-centered computing → HCI theory, concepts and models; Interaction paradigms; Empirical studies in HCI;

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Section: Emotional Analyticsmentioning

confidence: 99%

Progressive Disclosure

Springer

Whittaker

2020

ACM Trans. Interact. Intell. Syst.

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“…In these contexts, users might be less able to evaluate the results of algorithmic interpretations. In addition, emotion is highly variable between individuals and previous research demonstrates difficulty in accurately predicting emotion from text [47,69]. One of the major challenges with intelligent systems is handling errors; explaining algorithmic prediction in this difficult domain of emotional analytics allows us to better understand how users make sense of output that contains errors.…”

Section: Emotional Analyticsmentioning

confidence: 99%

Progressive disclosure

Springer

Whittaker

2019

Proceedings of the 24th International Conference on Intelligent User Interfaces

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As we increasingly delegate important decisions to intelligent systems, it is essential that users understand how algorithmic decisions are made. Prior work has often taken a technocentric approach to transparency. In contrast, we explore empirical user-centric methods to better understand user reactions to transparent systems. We assess user reactions to transparency in two studies. In Study 1, users anticipated that a more transparent system would perform better, but retracted this evaluation after experience with the system. Qualitative data suggest this arose because transparency is distracting and undermines simple heuristics users form about system operation. Study 2 explored these effects in depth, suggesting that users may benefit from initially simplified feedback that hides potential system errors and assists users in building working heuristics about system operation. We use these findings to motivate new progressive disclosure principles for transparency in intelligent systems.

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“…Previous work in NLP on affective events has primarily focused on identifying the affective polarity of events in narrative fables (Goyal et al, 2013), tweets (Li et al, 2014), news (Deng et al, 2013;Deng and Wiebe, 2014), and personal blogs (Ding and Riloff, 2016;Reed et al, 2017;Ding and Riloff, 2018b). Recently, further characterized affective events in terms of human needs categories: Physiological, Health, Leisure, Social, Financial, Cognition, and Freedom, which can explain why an event is positive or negative.…”

Section: Related Workmentioning

confidence: 99%

Improving Human Needs Categorization of Events with Semantic Classification

Ding¹,

Riloff²,

Feng³

2019

Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019)

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Human Needs categories have been used to characterize the reason why an affective event is positive or negative. For example, "I got the flu" and "I got fired" are both negative (undesirable) events, but getting the flu is a Health problem while getting fired is a Financial problem. Previous work created learning models to assign events to Human Needs categories based on their words and contexts. In this paper, we introduce an intermediate step that assigns words to relevant semantic concepts. We create lightly supervised models that learn to label words with respect to 10 semantic concepts associated with Human Needs categories, and incorporate these labels as features for event categorization. Our results show that recognizing relevant semantic concepts improves both the recall and precision of Human Needs categorization for events.

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Learning Lexico-Functional Patterns for First-Person Affect

Cited by 14 publications

References 21 publications

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Improving Human Needs Categorization of Events with Semantic Classification

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