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This study used semi-structured focus group interviews to examine specific literacy beliefs of 21 elementary pre-service teachers (PSTs) from five teacher education programs across the eastern, southern, and central parts of the USA. The interviews were transcribed and analyzed for statements about literacy beliefs. Each belief statement was also tagged as being positive (+), negative (-), or equal (=) to further identify the nuanced ways in which PSTs expressed each belief. Compared to findings in the field, this study (1) provides clearer explanations and categories of PSTs' literacy beliefs, (2) captures nuanced understanding of PSTs' literacy beliefs by examining their affective connotations, and (3) provides an emic perspective from PSTs across contexts. In doing so, the results expand on previous frameworks of literacy beliefs. Further, beliefs related to Teacher Strategy, Engagement, and Literacy Approach were discussed the most by PSTs. Literacy beliefs shared less frequently included beliefs about Culturally Responsive Teaching, and some Literacy Components (LC), such as, LC-Spelling, LC-Emergent Literacy, and LC-Fluency. Of the top three literacy beliefs shared by PSTs, references about Engagement were the most positive. The study suggests implications for literacy teacher educators. [ABSTRACT FROM AUTHOR]

This paper proposes a model-based method for real-time automatic mood estimation in video sequences. The approach is customized by learning the person's specific facial parameters, which are transformed into facial Action Units (AUs). A model mapping for mood representation is used to describe moods in terms of the PAD space: Pleasure, Arousal, and Dominance. From the intersection of these dimensions, eight octants represent fundamental mood categories. In the experimental evaluation, a stimulus video randomly selected from a set prepared to elicit different moods was played to participants, while the participant's facial expressions were recorded. From the experiment, Dominance is the dimension least impacted by facial expression, and this dimension could be eliminated from mood categorization. Then, four categories corresponding to the quadrants of the Pleasure–Arousal (PA) plane, "Exalted", "Calm", "Anxious" and "Bored", were defined, with two more categories for the "Positive" and "Negative" signs of the Pleasure (P) dimension. Results showed a 73% of coincidence in the PA categorization and a 94% in the P dimension, demonstrating that facial expressions can be used to estimate moods, within these defined categories, and provide cues for assessing users' subjective states in real-world applications. [ABSTRACT FROM AUTHOR]

In this paper we present a new speech emotion dataset on Spanish. The database is created using an elicited approach and is composed by fifty non-actors expressing the Ekman's six basic emotions of anger, disgust, fear, happiness, sadness, and surprise, plus neutral tone. This article describes how this database has been created from the recording step to the performed crowdsourcing perception test step. The crowdsourcing has facilitated to statistically validate the emotion of each collected audio sample and also to filter noisy data samples. Hence we obtained two datasets EmoSpanishDB and EmoMatchSpanishDB. The first includes those recorded audios that had consensus during the crowdsourcing process. The second selects from EmoSpanishDB only those audios whose emotion also matches with the originally elicited. Last, we present a baseline comparative study between different state of the art machine learning techniques in terms of accuracy, precision, and recall for both datasets. The results obtained for EmoMatchSpanishDB improves the ones obtained for EmoSpanishDB and thereof, we recommend to follow the methodology that was used for the creation of emotional databases. [ABSTRACT FROM AUTHOR]

This paper proposes a model-based method for real-time automatic mood estimation in video sequences. The approach is customized by learning the person’s specific facial parameters, which are transformed into facial Action Units (AUs). A model mapping for mood representation is used to describe moods in terms of the PAD space: Pleasure, Arousal, and Dominance. From the intersection of these dimensions, eight octants represent fundamental mood categories. In the experimental evaluation, a stimulus video randomly selected from a set prepared to elicit different moods was played to participants, while the participant’s facial expressions were recorded. From the experiment, Dominance is the dimension least impacted by facial expression, and this dimension could be eliminated from mood categorization. Then, four categories corresponding to the quadrants of the Pleasure–Arousal (PA) plane, “Exalted”, “Calm”, “Anxious” and “Bored”, were defined, with two more categories for the “Positive” and “Negative” signs of the Pleasure (P) dimension. Results showed a 73% of coincidence in the PA categorization and a 94% in the P dimension, demonstrating that facial expressions can be used to estimate moods, within these defined categories, and provide cues for assessing users’ subjective states in real-world applications.

This paper focuses on text-based affective computing for Jopara, a code-switching language that combines Guarani and Spanish. First, we collected a dataset of tweets primarily written in Guarani and annotated them for three widely used dimensions in sentiment analysis: (a) emotion recognition, (b) humor detection, and (c) offensive language identification. Then, we developed several neural network models, including large language models specifically designed for Guarani, and compared their performance against off-the-shelf multilingual and Spanish pre-trained models for the aforementioned dimensions. Our experiments show that language models incorporating Guarani during pre-training or pre-fine-tuning consistently achieve the best results, despite limited resources (a single 24-GB GPU and only 800K tokens). Notably, even a Guarani BERT model with just two layers of Transformers shows a favorable balance between accuracy and computational power, likely due to the inherent low-resource nature of the task. We present a comprehensive overview of corpus creation and model development for low-resource languages like Guarani, particularly in the context of its code-switching with Spanish, resulting in Jopara. Our findings shed light on the challenges and strategies involved in analyzing affective language in such linguistic contexts. [ABSTRACT FROM AUTHOR]

In multimodal affective analysis (MAA) tasks, the presence of heterogeneity among different modalities has propelled the exploration of the disentanglement methods as a pivotal area. Many emerging studies focus on disentangling the modality-invariant and modality-specific representations from input data and then fusing them for prediction. However, our study shows that modality-specific representations may contain information that is irrelevant or conflicting with the tasks, which downgrades the effectiveness of learned multimodal representations. We revisit the disentanglement issue, and propose a novel triple disentanglement approach, TriDiRA, which disentangles the modality-invariant, effective modality-specific and ineffective modality-specific representations from input data. By fusing only the modality-invariant and effective modality-specific representations, TriDiRA can significantly alleviate the impact of irrelevant and conflicting information across modalities during model training and prediction. Extensive experiments conducted on four benchmark datasets demonstrate the effectiveness and generalization of our triple disentanglement, which outperforms SOTA methods. The code is available at https://anonymous.4open.science/r/TriDiRA. • Propose a triple disentanglement multimodal representation learning method. • Design a dual-out attention output module for triple disentanglement. • Eliminate label-irrelevant representations from modality-specific representations. • Outperform SOTA methods on four benchmark datasets. [ABSTRACT FROM AUTHOR]

This paper propose a method to predict the stage of buzz-trend generation by analyzing the emotional information posted on social networking services for multimodal information, such as posted text and attached images, based on the content of the posts. The proposed method can analyze the diffusion scale from various angles, using only the information at the time of posting, when predicting in advance and the information of time error, when used for posterior analysis. Specifically, tweets and reply tweets were converted into vectors using the BERT general-purpose language model that was trained in advance, and the attached images were converted into feature vectors using a trained neural network model for image recognition. In addition, to analyze the emotional information of the posted content, we used a proprietary emotional analysis model to estimate emotions from tweets, reply tweets, and image features, which were then added to the input as emotional features. The results of the evaluation experiments showed that the proposed method, which added linguistic features (BERT vectors) and image features to tweets, achieved higher performance than the method using only a single feature. Although we could not observe the effectiveness of the emotional features, the more emotions a tweet and its reply match had, the more empathy action occurred and the larger the like and RT values tended to be, which could ultimately increase the likelihood of a tweet going viral. [ABSTRACT FROM AUTHOR]

Traditional video recommendation provides the viewers with customized media content according to their historical records (e.g., ratings, reviews). However, such systems tend to generate terrible results if the data is insufficient, which leads to a cold-start problem. An affective video recommender system (AVRS) is a multidiscipline and multimodal humanrobot interaction (HRI) system, and it incorporates physical, physiological, neuroscience, and computer science subjects and multimedia resources, including text, audio, and video. As a promising research domain, AVRS employs advanced affective analysis technologies in video resources; therefore, it can solve the cold-start problem. In AVRS, the viewers' emotional responses can be obtained from various techniques, including physical signals (e.g., facial expression, gestures, and speech) and internal signals (e.g., physiological signals). The changes in these signals can be detected when the viewers face specific situations. The physiological signals are a response to central and autonomic nervous systems and are mostly involuntarily activated, which cannot be easily controlled. Therefore, it is suitable for reliable emotion analysis. The physical signals can be recorded by a webcam or recorder. In contrast, the physiological signals can be collected by various equipment, e.g., psychophysiological heart rate (HR) signals calculated by echocardiogram (ECG), electro-dermal activity (EDA), and brain activity (GA) from electroencephalography (EEG) signals, skin conductance response (SCR) by a galvanic skin response (GSR), and photoplethysmography (PPG) estimating users' pulse. This survey aims to provide a comprehensive overview of the AVRS domain. To analyze the recent efforts in the field of affective video recommendation, we collected 92 relevant published articles from Google Scholar and summarized the articles and their key findings. In this survey, we feature these articles concerning AVRS from different perspectives, including various traditional recommendation algorithms and advanced deep learningbased algorithms, the commonly used affective video recommendation databases, audience response categories, and evaluation methods. Finally, we conclude the challenge of AVRS and provide the potential future research directions. [ABSTRACT FROM AUTHOR]

Now, the application of intelligent technologies such as machine learning and deep learning in natural language processing has achieved good results. This article studies the integration of emotion analysis in English module teaching of natural language processing. Vocabulary is a very important part in English teaching. Learning vocabulary well can improve students' reading ability. However, in the process of students' learning, vocabulary is the most basic and difficult to learn. Poor vocabulary learning and insufficient accumulation will restrict students' reading ability. Improving vocabulary teaching mode and learning methods can stimulate students' interest in learning and effectively improve their reading ability. In the third part of the article, the neural network language model and statistical model are used to analyze the key technologies of natural language processing, and then the Naive Bayes algorithm and support vector machine model algorithm are used to optimize the data. Finally, two classes are selected for comparative experiment, then, by integrating emotional teaching into students' classroom and analyzing students' interest, the conclusion is that integrating emotional teaching in teaching can effectively improve students' academic achievements, and at the same time, integrating emotional teaching in teaching can also stimulate students' enthusiasm for learning English and effectively change students' learning attitude. [ABSTRACT FROM AUTHOR]

People use Online Social Networks (OSNs) to express their opinions and feelings about many topics. Depending on the nature of an event and its dissemination rate in OSNs, and considering specific regions, the users' behavior can drastically change over a specific period of time. In this context, this work aims to propose an event detection system at the early stages of an event based on changes in the users' behavior in an OSN. This system can detect an event of any subject, and thus, it can be used for different purposes. The proposed event detection system is composed of the following main modules: (1) determination of the user's location, (2) message extraction from an OSN, (3) topic identification using natural language processing (NLP) based on the Deep Belief Network (DBN), (4) the user behavior change analyzer in the OSN, and (5) affective analysis for emotion identification based on a tree-convolutional neural network (tree-CNN). In the case of public health, the early event detection is very relevant for the population and the authorities in order to be able take corrective actions. Hence, the new coronavirus disease (COVID-19) is used as a case study in this work. For performance validation, the modules related to the topic identification and affective analysis were compared with other similar solutions or implemented with other machine learning algorithms. In the performance assessment, the proposed event detection system achieved an accuracy higher than 0.90, while other similar methods reached accuracy values less than 0.74. Additionally, our proposed system was able to detect an event almost three days earlier than the other methods. Furthermore, the information provided by the system permits to understand the predominant characteristics of an event, such as keywords and emotion type of messages.

The hybrid neural network model proposed in this paper consists of two main parts: extracting local features of text vectors by convolutional neural network, extracting global features related to text context by BiLSTM, and fusing the features extracted by the two complementary models. In this paper, the pre-processed sentences are put into the hybrid neural network for training. The trained hybrid neural network can automatically classify the sentences. When testing the algorithm proposed in this paper, the training corpus is Word2vec. The test results show that the accuracy rate of text categorization reaches 94.2%, and the number of iterations is 10. The results show that the proposed algorithm has high accuracy and good robustness when the sample size is seriously unbalanced. [ABSTRACT FROM AUTHOR]

Detecting emotions is very useful in many fields, from health-care to human-computer interaction. In this paper, we propose an iterative user-centered methodology for supporting the development of an emotion detection system based on low-cost sensors. Artificial Intelligence techniques have been adopted for emotion classification. Different kind of Machine Learning classifiers have been experimentally trained on the users' biometrics data, such as hearth rate, movement and audio. The system has been developed in two iterations and, at the end of each of them, the performance of classifiers (MLP, CNN, LSTM, Bidirectional-LSTM and Decision Tree) has been compared. After the experiment, the SAM questionnaire is proposed to evaluate the user's affective state when using the system. In the first experiment we gathered data from 47 participants, in the second one an improved version of the system has been trained and validated by 107 people. The emotional analysis conducted at the end of each iteration suggests that reducing the device invasiveness may affect the user perceptions and also improve the classification performance. [ABSTRACT FROM AUTHOR]

Objective: To explore the negative emotional characteristics of Weibo "Tree Hole" messages in order to provide ideas and data support for mental health promotion,suicide warning and intervention. Method:Using a "Tree Hole" artificial intelligence(AI) robot,351 825 messages were reviewed and words were divided by using a Jieba tool. The 500 most frequently used key words were calculated using a TF-IDF algorithm.Keywords co-occurrence network was constructed for social network analysis with important keywords by using Gephi software. Boson affective dictionary was used to judge for emotion characteristics of high frequency keywords. The amount of messages per hour was collected and the content characteristics of negative emotionswere analyzed. Results: Most messages were posted between 22:00 and 02:00, accounting for 36.3% of the total number of messages reviewed. The time period for posting the fewest number of messages was 05:00 to 07:00, accounting for 4.2% of the total number of messages reviewed. Among the 346 075 valid messages after data processing, 165 890 messages were positive emotional expression,among which 137 132 messages had an emotional score greater than 0, accounting for 39.7% of the total messages, 28 578 messages had an emotional score equal to 0(8.2%),and 180 185 messages were negative emotional expression,accounting for 52% of the total messages.Negative emotions were more expressed than positive emotions in each time period.The contents of the message included emotional expression, interpersonal relationship and social support, sleep and death.Among the people who express suicidal ideation, jumping off the building, sleeping pills, cutting wrists and burning charcoal were the most mentioned words related to suicidal ways.Conclusion: Negative emotions and related negative life events commonly are expressed by users of Weibo "tree hole". More attention should be paid to this phenomena, especially for users who express suicidal ideation,early identification and interventions are important. [ABSTRACT FROM AUTHOR]

Affective analysis of social media text is in great demand. Online text written in Chinese communities often contains mixed scripts including major text written in Chinese, an ideograph-based writing system, and minor text using Latin letters, an alphabet-based writing system. This phenomenon is referred to as writing systems changes (WSCs). Past studies have shown that WSCs often reflect unfiltered immediate affections. However, the use of WSCs poses more challenges in Natural Language Processing tasks because WSCs can break the syntax of the major text. In this work, we present our work to use WSCs as an effective feature in a hybrid deep learning model with attention network. The WSCs scripts are first identified by their encoding range. Then, the document representation of the text is learned through a Long Short-Term Memory model and the minor text is learned by a separate Convolution Neural Network model. To further highlight the WSCs components, an attention mechanism is adopted to re-weight the feature vector before the classification layer. Experiments show that the proposed hybrid deep learning method which better incorporates WSCs features can further improve performance compared to the state-of-the-art classification models. The experimental result indicates that WSCs can serve as effective information in affective analysis of the social media text. [ABSTRACT FROM AUTHOR]

• The protagonist with new definition is efficient to capture video emotional content. • Temporal information is useful to improve the emotion recognition performance. • A novel keyframe selecting strategy can extract typical protagonist-based frames. • Combining optical flow and CNN can extract discriminative features in time domain. • The proposed system can recognize the video emotion intelligently and automatically. Extracting an effective representation to analyze affection in videos is an inherent challenge. To address this problem, we present a novel emotion recognition system that can intelligently analyze and automatically recognize video emotions. We observe that most of the emotions in a video are closely related to the roles, especially those of the protagonist, which motivates us to explore how to utilize the protagonist's information to help recognize video emotions. By analyzing the traits of the protagonists, we suggest a new solution to detect the protagonists to adapt not only to the whole video but also to a video clip. Moreover, a new keyframe selection strategy based on the protagonist is designed to select a set of representative frames from video clips. Furthermore, the scale invariant feature transform (SIFT) features matrix, built from each keyframe, is fed into a convolutional neural network (CNN) to learn the discriminative representations, which makes the CNN and local features complement each other. Considering that emotions are usually continuous, we introduce temporal information into the CNN by using optical flow images. Additionally, we extract some handcrafted visual and audio features as a supplement. Finally, all the features, including the features learned from the CNN and the handcrafted features, are fused and input into a support vector machine (SVM) and a support vector regression (SVR) for video emotion recognition. The proposed system is validated on a public dataset (LIRIS-ACCEDE) and a new dataset (PMSZU). The experimental results demonstrate the promising performance of the proposed system, which achieves better performance than the compared methods. Our designed system for recognizing video emotions could also facilitate the development of similar video expert systems, for applications such as video recommendations, video classification and video retrieval. [ABSTRACT FROM AUTHOR]

Emotion recognition of music objects is a promising and important research issues in the field of music information retrieval. Usually, music emotion recognition could be considered as a training/classification problem. However, even given a benchmark (a training data with ground truth) and using effective classification algorithms, music emotion recognition remains a challenging problem. Most previous relevant work focuses only on acoustic music content without considering individual difference (i.e., personalization issues). In addition, assessment of emotions is usually self-reported (e.g., emotion tags) which might introduce inaccuracy and inconsistency. Electroencephalography (EEG) is a non-invasive brain-machine interface which allows external machines to sense neurophysiological signals from the brain without surgery. Such unintrusive EEG signals, captured from the central nervous system, have been utilized for exploring emotions. This paper proposes an evidence-based and personalized model for music emotion recognition. In the training phase for model construction and personalized adaption, based on the IADS (the International Affective Digitized Sound system, a set of acoustic emotional stimuli for experimental investigations of emotion and attention), we construct two predictive and generic models ANN1 (“EEG recordings of standardized group vs. emotions”) and ANN2 (“music audio content vs. emotion”). Both models are trained by an artificial neural network. We then collect a subject’s EEG recordings when listening the selected IADS samples, and apply the ANN1 to determine the subject’s emotion vector. With the generic model and the corresponding individual differences, we construct the personalized model H by the projective transformation. In the testing phase, given a music object, the processing steps are: (1) to extract features from the music audio content, (2) to apply ANN2 to calculate the vector in the arousal-valence emotion space, and (3) to apply the transformation matrix H to determine the personalized emotion vector. Moreover, with respect to a moderate music object, we apply a sliding window on the music object to obtain a sequence of personalized emotion vectors, in which those predicted vectors will be fitted and organized as an emotion trail for revealing dynamics in the affective content of music object. Experimental results suggest the proposed approach is effective. [ABSTRACT FROM AUTHOR]

Background: Building an effective team of developers is a complex task faced by both software companies and open source communities. The problem of forming a 'dream' team involves many variables, including consideration of human factors and it is not a dilemma solvable in a mathematical way. Empirical studies might provide interesting insights to explain which factors need to be taken into account in building a team of developers and which levers act to optimise productivity among developers. Aim: In this paper, we present the results of an empirical study aimed at investigating the link between team diversity (i.e., gender, nationality) and productivity (issue fixing time). Method: We consider issues solved from the GHTorrent dataset inferring gender and nationality of each team's members. We also evaluate the politeness of all comments involved in issue resolution. Results: Results show that higher gender diversity is linked with a lower team average issue fixing time (higher productivity), that nationality diversity is linked with lower team politeness and that gender diversity is linked with higher sentiment. [ABSTRACT FROM AUTHOR]

In this article, the author seeks to establish whether specific sites from Eastern Europe can be viewed as loci critiquing Pierre Nora’s seminal notion of lieux de mémoire. The sites in question are abandoned, clandestine locations of past violence and genocide, witnesses to wanton killings, today left with no memorial markers or inadequate ones. Without monuments, plaques, or fences, they might be understood as “completely forgotten,” as Claude Lanzmann once claimed. In opposition to that view, in the article the locations in question are interpreted as still potent agents in local processes of working with a traumatic past. Sites of mass violence and genocide are described as unheimlich and trigger strong affective reactions of fear, disgust, and shame whose actual causes remain unclear. This article analyzes possible catalysts of these powerful affective responses. The first hypothesis is grounded in the abundance of ghost stories in literary or artistic representations of the sites in question. The second hypothesis addresses the issue of the presence of dead bodies: human remains have never been properly neutralized by rituals. And finally, the third hypothesis explores the “effect of the affects” of non-sites of memory as the capacity of bodies to be moved by other bodies, the bodies affected in this case being those of the visitors to the uncanny sites. [ABSTRACT FROM AUTHOR]

Abstract: Most previous works on video classification and recommendation were only based on video contents, without considering the affective analysis of viewers. In this paper, we presented a novel method to classify and recommend videos based on affective analysis, mainly on facial expression recognition of viewers, by fusing the spatio–temporal features. For spatial features, we integrate Haar-like features into compositional ones according to the features’ correlation, and train a mid classifier. Then this process is embedded into the improved AdaBoost learning algorithm to obtain spatial features. And for temporal feature fusion, we adopt HDCRFs based on HCRFs by introducing a time dimension variable. The spatial features are embedded into HDCRFs to recognize facial expressions. Experiments on the Cohn–Kanada database show that the proposed method has a promising performance. Then viewers' changing facial expressions are collected frame by frame from the camera when they are watching videos. Finally, we draw affective curves which tell the process of affection changes. Through the curves, we segment each video into affective sections, classify videos into categories, and list recommendation scores. Experimental results on our collected database show that most subjects are satisfied with the classification and recommendation results. [Copyright &y& Elsevier]

The majority of existing work on sports video analysis concentrates on highlight extraction. Little work focuses on the important issue as how the extracted highlights should be organized. In this paper, we present a multimodal approach to organize the highlights extracted from racket sports video grounded on human behavior analysis using a nonlinear affective ranking model. Two research challenges of highlight ranking are addressed, namely affective feature extraction and ranking model construction. The basic principle of affective feature extraction in our work is to extract sensitive features which can stimulate user's emotion. Since the users pay most attention to player behavior and audience response in racket sport highlights, we extract affective features from player behavior including action and trajectory, and game-specific audio keywords. We propose a novel motion analysis method to recognize the player actions. We employ support vector regression to construct the nonlinear highlight ranking model from affective features. A new subjective evaluation criterion is proposed to guide the model construction. To evaluate the performance of the proposed approaches, we have tested them on more than ten-hour broadcast tennis and badminton videos. The experimental results demonstrate that our action recognition approach significantly outperforms the existing appearance-based method. Moreover, our user study shows that the affective highlight ranking approach is effective. [ABSTRACT FROM PUBLISHER]