1,935 research outputs found
Affect Analysis of Radical Contents on Web Forums Using SentiWordNet
The internet has become a major tool for communication, training, fundraising, media operations, and recruitment, and these processes often use web forums. This paper presents a model that was built using SentiWordNet, WordNet and NLTK to analyze selected web forums that included radical content. SentiWordNet is a lexical resource for supporting opinion mining by assigning a positivity score and a negativity score to each WordNet. The approaches of the model measure and identify sentiment polarity and affect the intensity of that which appears in the web forum. The results show that SentiWordNet can be used for analyzing sentences that appear in web forums
A new multi-modal dataset for human affect analysis
In this paper we present a new multi-modal dataset of spontaneous three way human interactions. Participants were recorded in an unconstrained environment at various locations during a sequence of debates in a video conference, Skype style arrangement. An additional depth modality was introduced, which permitted the capture of 3D information in addition to the video and audio signals. The dataset consists of 16 participants and is subdivided into 6 unique sections. The dataset was manually annotated on a continuously scale across 5 different affective dimensions including arousal, valence, agreement, content and interest.
The annotation was performed by three human annotators with the ensemble average calculated for use in the dataset. The corpus enables the analysis of human affect during conversations in a real life scenario. We first briefly reviewed the existing affect dataset and the methodologies
related to affect dataset construction, then we detailed how our unique dataset was constructed
Deep neural network augmentation: generating faces for affect analysis
This paper presents a novel approach for synthesizing facial affect; either in terms of the six basic expressions (i.e., anger, disgust, fear, joy, sadness and surprise), or in terms of valence (i.e., how positive or negative is an emotion) and arousal (i.e., power of the emotion activation). The proposed approach accepts the following inputs:(i) a neutral 2D image of a person; (ii) a basic facial expression or a pair of valence-arousal (VA) emotional state descriptors to be generated, or a path of affect in the 2D VA space to be generated as an image sequence. In order to synthesize affect in terms of VA, for this person, 600,000 frames from the 4DFAB database were annotated. The affect synthesis is implemented by fitting a 3D Morphable Model on the neutral image, then deforming the reconstructed face and adding the inputted affect, and blending the new face with the given affect into the original image. Qualitative experiments illustrate the generation of realistic images, when the neutral image is sampled from fifteen well known lab-controlled or in-the-wild databases, including Aff-Wild, AffectNet, RAF-DB; comparisons with generative adversarial networks (GANs) show the higher quality achieved by the proposed approach. Then, quantitative experiments are conducted, in which the synthesized images are used for data augmentation in training deep neural networks to perform affect recognition over all databases; greatly improved performances are achieved when compared with state-of-the-art methods, as well as with GAN-based data augmentation, in all cases
Your fellows matter: Affect analysis across subjects in group videos
Automatic affect analysis has become a well established
research area in the last two decades. Recent works have
started moving from individual to group scenarios. However,
little attention has been paid to investigating how individuals in
a group influence the affective states of each other. In this paper,
we propose a novel framework for cross-subjects affect analysis
in group videos. Specifically, we analyze the correlation of the
affect among group members and investigate the automatic
recognition of the affect of one subject using the behaviours
expressed by another subject in the same group. A set of
experiments are conducted using a recently collected database
aimed at affect analysis in group settings. Our results show
that (1) people in the same group do share more information
in terms of behaviours and emotions than people in different
groups; and (2) the affect of one subject in a group can be
better predicted using the expressive behaviours of another
subject within the same group than using that of a subject
from a different group. This work is of great importance for
affect recognition in group settings: when the information of
one subject is unavailable due to occlusion, head/body poses
etc., we can predict his/her affect by employing the expressive
behaviours of the other subject(s).European Unions Horizon 202
Graph-based Facial Affect Analysis: A Review of Methods, Applications and Challenges
Facial affect analysis (FAA) using visual signals is important in
human-computer interaction. Early methods focus on extracting appearance and
geometry features associated with human affects, while ignoring the latent
semantic information among individual facial changes, leading to limited
performance and generalization. Recent work attempts to establish a graph-based
representation to model these semantic relationships and develop frameworks to
leverage them for various FAA tasks. In this paper, we provide a comprehensive
review of graph-based FAA, including the evolution of algorithms and their
applications. First, the FAA background knowledge is introduced, especially on
the role of the graph. We then discuss approaches that are widely used for
graph-based affective representation in literature and show a trend towards
graph construction. For the relational reasoning in graph-based FAA, existing
studies are categorized according to their usage of traditional methods or deep
models, with a special emphasis on the latest graph neural networks.
Performance comparisons of the state-of-the-art graph-based FAA methods are
also summarized. Finally, we discuss the challenges and potential directions.
As far as we know, this is the first survey of graph-based FAA methods. Our
findings can serve as a reference for future research in this field.Comment: 20 pages, 12 figures, 5 table
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