14,893 research outputs found
Looking Beyond a Clever Narrative: Visual Context and Attention are Primary Drivers of Affect in Video Advertisements
Emotion evoked by an advertisement plays a key role in influencing brand
recall and eventual consumer choices. Automatic ad affect recognition has
several useful applications. However, the use of content-based feature
representations does not give insights into how affect is modulated by aspects
such as the ad scene setting, salient object attributes and their interactions.
Neither do such approaches inform us on how humans prioritize visual
information for ad understanding. Our work addresses these lacunae by
decomposing video content into detected objects, coarse scene structure, object
statistics and actively attended objects identified via eye-gaze. We measure
the importance of each of these information channels by systematically
incorporating related information into ad affect prediction models. Contrary to
the popular notion that ad affect hinges on the narrative and the clever use of
linguistic and social cues, we find that actively attended objects and the
coarse scene structure better encode affective information as compared to
individual scene objects or conspicuous background elements.Comment: Accepted for publication in the Proceedings of 20th ACM International
Conference on Multimodal Interaction, Boulder, CO, US
An Affect-Rich Neural Conversational Model with Biased Attention and Weighted Cross-Entropy Loss
Affect conveys important implicit information in human communication. Having
the capability to correctly express affect during human-machine conversations
is one of the major milestones in artificial intelligence. In recent years,
extensive research on open-domain neural conversational models has been
conducted. However, embedding affect into such models is still under explored.
In this paper, we propose an end-to-end affect-rich open-domain neural
conversational model that produces responses not only appropriate in syntax and
semantics, but also with rich affect. Our model extends the Seq2Seq model and
adopts VAD (Valence, Arousal and Dominance) affective notations to embed each
word with affects. In addition, our model considers the effect of negators and
intensifiers via a novel affective attention mechanism, which biases attention
towards affect-rich words in input sentences. Lastly, we train our model with
an affect-incorporated objective function to encourage the generation of
affect-rich words in the output responses. Evaluations based on both perplexity
and human evaluations show that our model outperforms the state-of-the-art
baseline model of comparable size in producing natural and affect-rich
responses.Comment: AAAI-1
Evaluating Content-centric vs User-centric Ad Affect Recognition
Despite the fact that advertisements (ads) often include strongly emotional
content, very little work has been devoted to affect recognition (AR) from ads.
This work explicitly compares content-centric and user-centric ad AR
methodologies, and evaluates the impact of enhanced AR on computational
advertising via a user study. Specifically, we (1) compile an affective ad
dataset capable of evoking coherent emotions across users; (2) explore the
efficacy of content-centric convolutional neural network (CNN) features for
encoding emotions, and show that CNN features outperform low-level emotion
descriptors; (3) examine user-centered ad AR by analyzing Electroencephalogram
(EEG) responses acquired from eleven viewers, and find that EEG signals encode
emotional information better than content descriptors; (4) investigate the
relationship between objective AR and subjective viewer experience while
watching an ad-embedded online video stream based on a study involving 12
users. To our knowledge, this is the first work to (a) expressly compare user
vs content-centered AR for ads, and (b) study the relationship between modeling
of ad emotions and its impact on a real-life advertising application.Comment: Accepted at the ACM International Conference on Multimodal Interation
(ICMI) 201
An Agent-Based Model of Collective Emotions in Online Communities
We develop a agent-based framework to model the emergence of collective
emotions, which is applied to online communities. Agents individual emotions
are described by their valence and arousal. Using the concept of Brownian
agents, these variables change according to a stochastic dynamics, which also
considers the feedback from online communication. Agents generate emotional
information, which is stored and distributed in a field modeling the online
medium. This field affects the emotional states of agents in a non-linear
manner. We derive conditions for the emergence of collective emotions,
observable in a bimodal valence distribution. Dependent on a saturated or a
superlinear feedback between the information field and the agent's arousal, we
further identify scenarios where collective emotions only appear once or in a
repeated manner. The analytical results are illustrated by agent-based computer
simulations. Our framework provides testable hypotheses about the emergence of
collective emotions, which can be verified by data from online communities.Comment: European Physical Journal B (in press), version 2 with extended
introduction, clarification
Affective games:a multimodal classification system
Affective gaming is a relatively new field of research that exploits human emotions to influence gameplay for an enhanced player experience. Changes in player’s psychology reflect on their behaviour and physiology, hence recognition of such variation is a core element in affective games. Complementary sources of affect offer more reliable recognition, especially in contexts where one modality is partial or unavailable. As a multimodal recognition system, affect-aware games are subject to the practical difficulties met by traditional trained classifiers. In addition, inherited game-related challenges in terms of data collection and performance arise while attempting to sustain an acceptable level of immersion. Most existing scenarios employ sensors that offer limited freedom of movement resulting in less realistic experiences. Recent advances now offer technology that allows players to communicate more freely and naturally with the game, and furthermore, control it without the use of input devices. However, the affective game industry is still in its infancy and definitely needs to catch up with the current life-like level of adaptation provided by graphics and animation
Speech-based recognition of self-reported and observed emotion in a dimensional space
The differences between self-reported and observed emotion have only marginally been investigated in the context of speech-based automatic emotion recognition. We address this issue by comparing self-reported emotion ratings to observed emotion ratings and look at how differences between these two types of ratings affect the development and performance of automatic emotion recognizers developed with these ratings. A dimensional approach to emotion modeling is adopted: the ratings are based on continuous arousal and valence scales. We describe the TNO-Gaming Corpus that contains spontaneous vocal and facial expressions elicited via a multiplayer videogame and that includes emotion annotations obtained via self-report and observation by outside observers. Comparisons show that there are discrepancies between self-reported and observed emotion ratings which are also reflected in the performance of the emotion recognizers developed. Using Support Vector Regression in combination with acoustic and textual features, recognizers of arousal and valence are developed that can predict points in a 2-dimensional arousal-valence space. The results of these recognizers show that the self-reported emotion is much harder to recognize than the observed emotion, and that averaging ratings from multiple observers improves performance
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