IEEE Transactions on Affective Computing

Social media platforms like Twitter act as the medium for communication among people, government agencies, NGOs, and other relief providing agencies in widespread humanitarian havoc during a disaster outbreak when other communication means might not be ...

Multimodal emotion recognition (MER) aims to understand the way that humans express their emotions by exploring complementary information across modalities. However, it is hard to guarantee that full-modality data is always available in real-world ...

Identifying sarcastic clues from both textual and visual information has become an important research issue, called Multimodal Sarcasm Detection. In this article, we investigate multimodal sarcasm detection from a novel perspective, where a multimodal ...

Although video affective content analysis has great potential in many applications, it has not been thoroughly studied due to limited datasets. In this article, we construct a large-scale video affective dataset with danmu (VAD). It consists of 19,267 ...

Electroencephalography (EEG)-based emotion recognition plays a key role in the development of affective brain-computer interfaces (BCIs). However, emotions are complex and extracting salient EEG features underlying distinct emotional states is inherently ...

Multimodal emotion recognition in conversation (ERC) has garnered growing attention from research communities in various fields. In this paper, we propose a Cross-modal Fusion Network with Emotion-Shift Awareness (CFN-ESA) for ERC. Extant approaches ...

Emotion Cause Pair Extraction (ECPE) aims to extract emotions and their causes from a document. Powerful emotion and cause extraction abilities have proven essential in achieving accurate ECPE. However, most existing methods employ shared feature learning ...

Non-Playable Characters (NPCs) are a subtype of virtual agents that populate video games by endorsing social roles in the narrative. To infer NPCs’ roles, players evaluate NPCs’ appearance and behaviors, usually by ascribing human traits to ...

To classify the sentiment polarity of the aspect entity in a sentence, most existing research evaluates the semantic knowledge among a certain aspect of a sentence and corresponding context as significant clues for the task. However, available ...

Although multimodal physiological data from the central and peripheral nervous systems can objectively respond to human emotional states, the individual differences caused by non-stationary and low signal-to-noise properties bring several challenges to ...

Recently, depression recognition through EEG has gained significant attention. However, two challenges have not been properly addressed in prior automated depression recognition and classification studies: 1) EEG data lacks an explicit topological ...

Emotion-cause pair extraction (ECPE) aims to extract emotions and their corresponding cause from a document. The previous works have made great progress. However, there exist two major issues in existing works. First, most existing works mainly focus on ...

In electroencephalographic-based (EEG-based) emotion recognition, high non-stationarity and individual differences in EEG signals could lead to significant discrepancies between sessions/subjects, making generalization to a new session/subject very ...

To develop reliable, valid, and efficient measures of obsessive-compulsive disorder (OCD) severity, comorbid depression severity, and total electrical energy delivered (TEED) by deep brain stimulation (DBS), we trained and compared random forests ...

In multichannel electroencephalograph (EEG) emotion recognition, most graph-based studies employ shallow graph model for spatial characteristics learning due to node over-smoothing caused by an increase in network depth. To address over-smoothing, we ...

Emotion recognition based on EEG signals has been a challenging task. The acquisition of EEG signals is complex, time-consuming, and has a high overhead. Artificial Intelligence Generated Content technology has been developing rapidly in image and sound ...

Existing multimodal sentiment analysis models focus more on fusing highly correlated image-text pairs, and thus achieves unsatisfactory performance on multimodal social media data which usually manifests weak correlations between different modalities. To ...

Micro-expression (ME) is an instinctive reaction that is not controlled by thoughts. It reveals one's inner feelings, which is significant in sentiment analysis and lie detection. Since micro-expression is expressed as subtle facial changes within ...

Aspect Sentiment Triplet Extraction (ASTE) is an essential task in fine-grained opinion mining and sentiment analysis that involves extracting triplets consisting of aspect terms, opinion terms, and their associated sentiment polarities from texts. While ...

The blossoming semi-automated vehicles allow drivers to engage in various non-driving-related tasks, which may stimulate diverse emotions, thus affecting takeover safety. Though the effects of emotion on takeover behavior have recently been examined, how ...

Emotions deeply influence human behavior and decision-making. Currently, the spatiotemporal joint neural processing pattern of human emotions remains largely unclear. This study employed EEG-fNIRS simultaneous recordings to capture the spatiotemporal ...

Automatic personality assessment (APA) has immense potential to improve decision-making and human-machine interaction. Numerous techniques for APA have been proposed in existing literature, with prior psychological studies demonstrating convergent ...

As one of the world's most prevalent mental illnesses, depression is not easy to detect since it affects different people in different ways. Recently, linguistic features extracted from transcribed texts have been widely explored in depression ...

In creativity support and computational co-creativity contexts, the task of discovering appropriate prompts for use with text-to-image generative models remains difficult. In many cases the creator wishes to evoke a certain impression with the image, but ...

Emotional expression is a bridge to human communication, especially for the hearing impaired. This paper proposes a sign language emotion recognition method based on semantic and movement features by exploring the relationship between emotion valence and ...

Building systems for predicting human socio-emotional states has promising applications; however, if trained on biased data, such systems could inadvertently yield biased decisions. Bias mitigation remains an open problem, which tackles the correction of ...

With the rise of foundation models, a new artificial intelligence paradigm has emerged, by simply using general purpose foundation models with prompting to solve problems instead of training a separate machine learning model for each problem. Such models ...