Query: emotion

title: The Expression of Emotion in Man and Animals
abstract: ION. A. ABSTRACTION, 226. Actions, reflex, 35 coughing, sneezing, &c. 85; muscular action of decapitated frog, 36; closing the eyelids, 38 starting, 3841; contraction of the iris, 41.  Affirmation, signs of. 272. Albinos, blushing in, 312, 326.  Ambition, 261. Anatomical drawin,s by HeDle, 5. Anatomy and Philosophy of Expression, 2. Anderson, Dr. 106, n. 26. Anger, as a stimulant, 79; expreqsion, 244; in monkeys, 136. See also Rage. Animals, special expressions of, 115.  7 habitual associated movements in the lower, 42 49; dogs, 43; wolves and Jackals, 44;

title: EEG Emotion Recognition Using Dynamical Graph Convolutional Neural Networks
abstract: In this paper, a multichannel EEG emotion recognition method based on a novel dynamical graph convolutional neural networks (DGCNN) is proposed. The basic idea of the proposed EEG emotion recognition method is to use a graph to model the multichannel EEG features and then perform EEG emotion classification based on this model. Different from the traditional graph convolutional neural networks (GCNN) methods, the proposed DGCNN method can dynamically learn the intrinsic relationship between different electroencephalogram (EEG) channels, represented by an adjacency matrix, via training a neural network so as to benefit for more discriminative EEG feature extraction. Then, the learned adjacency matrix is used to learn more discriminative features for improving the EEG emotion recognition. We conduct extensive experiments on the SJTU emotion EEG dataset (SEED) and DREAMER dataset. The experimental results demonstrate that the proposed method achieves better recognition performance than the state of the art methods, in which the average recognition accuracy of 90.4 percent is achieved for subject dependent experiment while 79.95 percent for subject independent cross validation one on the SEED database, and the average accuracies of 86.23, 84.54 and 85.02 percent are respectively obtained for valence, arousal and dominance classifications on the DREAMER database.

title: Emotion Recognition on Twitter: Comparative Study and Training a Unison Model
abstract: Despite recent successes of deep learning in many fields of natural language processing, previous studies of emotion recognition on Twitter mainly focused on the use of lexicons and simple classifiers on bag of words models. The central question of our study is whether we can improve their performance using deep learning. To this end, we exploit hashtags to create three large emotion labeled data sets corresponding to different classifications of emotions. We then compare the performance of several word and character based recurrent and convolutional neural networks with the performance on bag of words and latent semantic indexing models. We also investigate the transferability of the final hidden state representations between different classifications of emotions, and whether it is possible to build a unison model for predicting all of them using a shared representation. We show that recurrent neural networks, especially character based ones, can improve over bag of words and latent semantic indexing models. Although the transfer capabilities of these models are poor, the newly proposed training heuristic produces a unison model with performance comparable to that of the three single models.

title: Identifying Stable Patterns over Time for Emotion Recognition from EEG
abstract: In this paper, we investigate stable patterns of electroencephalogram (EEG) over time for emotion recognition using a machine learning approach. Up to now, various findings of activated patterns associated with different emotions have been reported. However, their stability over time has not been fully investigated yet. In this paper, we focus on identifying EEG stability in emotion recognition. We systematically evaluate the performance of various popular feature extraction, feature selection, feature smoothing and pattern classification methods with the DEAP dataset and a newly developed dataset called SEED for this study. Discriminative Graph regularized Extreme Learning Machine with differential entropy features achieves the best average accuracies of 69.67 and 91.07 percent on the DEAP and SEED datasets, respectively. The experimental results indicate that stable patterns exhibit consistency across sessions; the lateral temporal areas activate more for positive emotions than negative emotions in beta and gamma bands; the neural patterns of neutral emotions have higher alpha responses at parietal and occipital sites; and for negative emotions, the neural patterns have significant higher delta responses at parietal and occipital sites and higher gamma responses at prefrontal sites. The performance of our emotion recognition models shows that the neural patterns are relatively stable within and between sessions.

title: Emotional Expressions Reconsidered: Challenges to Inferring Emotion From Human Facial Movements
abstract: It is commonly assumed that a person's emotional state can be readily inferred from his or her facial movements, typically called emotional expressions or facial expressions. This assumption influences legal judgments, policy decisions, national security protocols, and educational practices; guides the diagnosis and treatment of psychiatric illness, as well as the development of commercial applications; and pervades everyday social interactions as well as research in other scientific fields such as artificial intelligence, neuroscience, and computer vision. In this article, we survey examples of this widespread assumption, which we refer to as the common view, and we then examine the scientific evidence that tests this view, focusing on the six most popular emotion categories used by consumers of emotion research: anger, disgust, fear, happiness, sadness, and surprise. The available scientific evidence suggests that people do sometimes smile when happy, frown when sad, scowl when angry, and so on, as proposed by the common view, more than what would be expected by chance. Yet how people communicate anger, disgust, fear, happiness, sadness, and surprise varies substantially across cultures, situations, and even across people within a single situation. Furthermore, similar configurations of facial movements variably express instances of more than one emotion category. In fact, a given configuration of facial movements, such as a scowl, often communicates something other than an emotional state. Scientists agree that facial movements convey a range of information and are important for social communication, emotional or otherwise. But our review suggests an urgent need for research that examines how people actually move their faces to express emotions and other social information in the variety of contexts that make up everyday life, as well as careful study of the mechanisms by which people perceive instances of emotion in one another. We make specific research recommendations that will yield a more valid picture of how people move their faces to express emotions and how they infer emotional meaning from facial movements in situations of everyday life. This research is crucial to provide consumers of emotion research with the translational information they require.

title: MELD: A Multimodal Multi Party Dataset for Emotion Recognition in Conversations
abstract: Emotion recognition in conversations is a challenging task that has recently gained popularity due to its potential applications. Until now, however, a large scale multimodal multi party emotional conversational database containing more than two speakers per dialogue was missing. Thus, we propose the Multimodal EmotionLines Dataset (MELD) an extension and enhancement of EmotionLines. MELD contains about 13,000 utterances from 1,433 dialogues from the TV series Friends. Each utterance is annotated with emotion and sentiment labels, and encompasses audio, visual and textual modalities. We propose several strong multimodal baselines and show the importance of contextual and multimodal information for emotion recognition in conversations. The full dataset is available for use at http:/affective meld.github.io.

title: Label less Learning for Emotion Cognition
abstract: In this paper, we propose a label less learning for emotion cognition (LLEC) to achieve the utilization of a large amount of unlabeled data. We first inspect the unlabeled data from two perspectives, i.e. the feature layer and the decision layer. By utilizing the similarity model and the entropy model, this paper presents a hybrid label less learning that can automatically label data without human intervention. Then, we design an enhanced hybrid label less learning to purify the automatic labeled data. To further improve the accuracy of emotion detection model and increase the utilization of unlabeled data, we apply enhanced hybrid label less learning for multimodal unlabeled emotion data. Finally, we build a real world test bed to evaluate the LLEC algorithm. The experimental results show that the LLEC algorithm can improve the accuracy of emotion detection significantly.

title: A Mutual Information Based Adaptive Windowing of Informative EEG for Emotion Recognition
abstract: Emotion recognition using brain wave signals involves using high dimensional electroencephalogram (EEG) data. In this paper, a window selection method based on mutual information is introduced to select an appropriate signal window to reduce the length of the signals. The motivation of the windowing method comes from EEG emotion recognition being computationally costly and the data having low signal to noise ratio. The aim of the windowing method is to find a reduced signal where the emotions are strongest. In this paper, it is suggested, that using only the signal section which best describes emotions improves the classification of emotions. This is achieved by iteratively comparing different length EEG signals at different time locations using the mutual information between the reduced signal and emotion labels as criterion. The reduced signal with the highest mutual information is used for extracting the features for emotion classification. In addition, a viable framework for emotion recognition is introduced. Experimental results on publicly available datasets, DEAP and MAHNOB HCI, show significant improvement in emotion recognition accuracy.

title: The Role of Emotion

title: Developing an Understanding of Emotion Categories: Lessons from Objects
abstract: How and when infants and young children begin to develop emotion categories is not yet well understood. Research has largely treated the learning problem as one of identifying perceptual similarities among exemplars (typically posed, stereotyped facial configurations) However, recent meta analyses and reviews converge to suggest that emotion categories are abstract, involving high dimensional and situationally variable instances. In this paper we consult research on the development of abstract object categorization to guide hypotheses about how infants might learn abstract emotion categories because the two domains present infants with similar learning challenges. In particular, we consider how a developmental cascades framework offers opportunities to understand how and when young children develop emotion categories.