Emotion Classification Model Research Articles

The influence of music teaching appreciation on the mental health of college students based on multimedia data analysis.

The mental health problem of college students has gradually become the focus of people's attention. The music appreciation course in university is a very effective approach of psychological counseling, and it is urgent to explore the role of music appreciation in psychological adjustment. Therefore, we propose an emotion classification model based on particle swarm optimization (PSO) to study the effect of inter active music appreciation teaching on the mental health of college students. We first extract musical features as input. Then, the extracted music appreciation features generate subtitles of music information. Finally, we weight the above features, input them into the network, modify the network through particle swarm optimization, and output the emotional class of music. The experimental results show that the music emotion classification model has a high classification accuracy of 82.6%, and can obtain the emotional categories included in interactive music appreciation, which is helpful to guide the mental health of college students in music appreciation teaching.

Deep feature extraction from EEG signals using xception model for emotion classification

Deep feature extraction from EEG signals using xception model for emotion classification

EEG-Based Emotion Recognition with Consideration of Individual Difference.

Electroencephalograms (EEGs) are often used for emotion recognition through a trained EEG-to-emotion models. The training samples are EEG signals recorded while participants receive external induction labeled as various emotions. Individual differences such as emotion degree and time response exist under the same external emotional inductions. These differences can lead to a decrease in the accuracy of emotion classification models in practical applications. The brain-based emotion recognition model proposed in this paper is able to sufficiently consider these individual differences. The proposed model comprises an emotion classification module and an individual difference module (IDM). The emotion classification module captures the spatial and temporal features of the EEG data, while the IDM introduces personalized adjustments to specific emotional features by accounting for participant-specific variations as a form of interference. This approach aims to enhance the classification performance of EEG-based emotion recognition for diverse participants. The results of our comparative experiments indicate that the proposed method obtains a maximum accuracy of 96.43% for binary classification on DEAP data. Furthermore, it performs better in scenarios with significant individual differences, where it reaches a maximum accuracy of 98.92%.

Uni2Mul: A Conformer-Based Multimodal Emotion Classification Model by Considering Unimodal Expression Differences with Multi-Task Learning

Multimodal emotion classification (MEC) has been extensively studied in human–computer interaction, healthcare, and other domains. Previous MEC research has utilized identical multimodal annotations (IMAs) to train unimodal models, hindering the learning of effective unimodal representations due to differences between unimodal expressions and multimodal perceptions. Additionally, most MEC fusion techniques fail to consider the unimodal–multimodal inconsistencies. This study addresses two important issues in MEC: learning satisfactory unimodal representations of emotion and accounting for unimodal–multimodal inconsistencies during the fusion process. To tackle these challenges, the authors propose the Two-Stage Conformer-based MEC model (Uni2Mul) with two key innovations: (1) in stage one, unimodal models are trained using independent unimodal annotations (IUAs) to optimize unimodal emotion representations; (2) in stage two, a Conformer-based architecture is employed to fuse the unimodal representations learned in stage one and predict IMAs, accounting for unimodal–multimodal differences. The proposed model is evaluated on the CH-SIMS dataset. The experimental results demonstrate that Uni2Mul outperforms baseline models. This study makes two key contributions: (1) the use of IUAs improves unimodal learning; (2) the two-stage approach addresses unimodal–multimodal inconsistencies during Conformer-based fusion. Uni2Mul advances MEC by enhancing unimodal representation learning and Conformer-based fusion.

Automatic music emotion classification model for movie soundtrack subtitling based on neuroscientific premises

The ability of music to induce emotions has been arousing a lot of interest in recent years, especially due to the boom in music streaming platforms and the use of automatic music recommenders. Music Emotion Recognition approaches are based on combining multiple audio features extracted from digital audio samples and different machine learning techniques. In these approaches, neuroscience results on musical emotion perception are not considered. The main goal of this research is to facilitate the automatic subtitling of music. The authors approached the problem of automatic musical emotion detection in movie soundtracks considering these characteristics and using scientific musical databases, which have become a reference in neuroscience research. In the experiments, the Constant-Q-Transform spectrograms, the ones that best represent the relationships between musical tones from the point of view of human perception, are combined with Convolutional Neural Networks. Results show an efficient emotion classification model for 2-second musical audio fragments representative of intense basic feelings of happiness, sadness, and fear. Those emotions are the most interesting to be identified in the case of movie music captioning. The quality metrics have demonstrated that the results of the different models differ significantly and show no homogeneity. Finally, these results pave the way for an accessible and automatic captioning of music, which could automatically identify the emotional intent of the different segments of the movie soundtrack.

On the use of sentiment analysis for linguistics research. Observations on sentiment polarity and the use of the progressive in Italian.

This article offers a conceptual and methodological contribution to linguistics by exploring the potential value of using sentiment analysis (SA) for research in this field. Firstly, it discusses the limitations and advantages of using SA for linguistics research including the wider epistemological implications of its application outside of its original conception as a product reviews analysis tool. Methodologically, it tests its applicability against an established linguistic case: the correlation between subjective attitudes such as surprise, irritation and discontent and the use of the progressive. The language example is Italian for which this function of the progressive form has not been analyzed yet. The analysis applies FEEL-IT, a state-of-the-art transformer-based machine learning model for emotion and sentiment classification in Italian on language samples from various sources as collected in Evalita-2014 (238,556 words). The results show statistically significant correlations between negative subjective attitudes and the use of the progressive in line with previous accounts in other languages. The article concludes with a few additional propositions for practitioners and researchers using SA.

Retracted: Personalized Online Education Learning Strategies Based on Transfer Learning Emotion Classification Model

Retracted: Personalized Online Education Learning Strategies Based on Transfer Learning Emotion Classification Model

USING LONG SHORT-TERM MEMORY NETWORKS FOR NATURAL LANGUAGE PROCESSING

The problem of emotion classification is a complex and non-trivial task of language interpretation due to the natural language structure and its dynamic nature. The significance of the study is in covering the important issue of automatic processing of client feedbacks, collecting opinions and trend-catching. In this work, a number of existing solutions for emotion classification problem were considered, having their shortcomings and advantages illustrated. The evaluation of performance of the considered models was conducted on emotion classification on four emotion classes, namely Happy, Sad, Angry and Others. The model for emotion classification in three-sentence conversations was proposed in this work. The model is based on smileys and word embeddings with domain specificity in state of art conversations on the Internet. The importance of taking into account the information extracted from smileys as an additional data source of emotional coloring is investigated. The model performance is evaluated and compared with language processing model BERT (Bidirectional Encoder Representations from Transformers). The proposed model achieved better performance at classifying emotions comparing to BERT (having F1 score as 78 versus 75). It should be noted, that further study should be performed to enhance the processing by the model of mixed reviews represented by emotion class Others. However, modern performance of models for language representation and understanding did not achieve the human performance. There is a variety of factors to consider when choosing the word embeddings and training methods to design the model architecture.

Developing a machine learning model for detecting depression, anxiety, and apathy in older adults with mild cognitive impairment using speech and facial expressions: A cross-sectional observational study

BackgroundDepression, anxiety, and apathy are highly prevalent in older people with preclinical dementia and mild cognitive impairment. These symptoms have also proven valuable in predicting the progression from mild cognitive impairment to dementia, enabling a timely diagnosis and treatment. However, objective and reliable indicators to detect and distinguish depression, anxiety, and apathy are relatively scarce. ObjectiveThis study aimed to develop a machine learning model to detect and distinguish depression, anxiety, and apathy based on speech and facial expressions. DesignAn observational, cross-sectional study design. Setting(s)The memory outpatient department of a tertiary hospital. Participants319 older adults diagnosed with mild cognitive impairment. MethodsDepression, anxiety, and apathy were evaluated by the Public Health Questionnaire, General Anxiety Disorder, and Apathy Evaluation Scale, respectively. Speech and facial expressions of older adults with mild cognitive impairment were digitally captured using audio and video recording software. Open-source data analysis toolkits were utilized to extract speech, facial, and text features. The multiclass classification was used to develop classification models, and shapely additive explanations were used to explain the contribution of each feature within the model. ResultsThe random forest method was used to develop a multiclass emotion classification model, which performed well in classifying emotions with a weighted-average F1 score of 96.6 %. The model also demonstrated high accuracy, precision, and recall, with 87.4 %, 86.6 %, and 87.6 %, respectively. ConclusionsThe machine learning model developed in this study demonstrated strong classification performance in detecting and differentiating depression, anxiety, and apathy. This innovative approach combines text, audio, and video to provide objective methods for precise classification and remote monitoring of these symptoms in nursing practice. RegistrationThis study was registered at the Chinese Clinical Trial Registry (registration number: ChiCTR1900023892; registration date: June 19th, 2019).

A Study on the Voice-Based Emotional Classification Model for Senior Healthcare

A Study on the Voice-Based Emotional Classification Model for Senior Healthcare

Detecting Clinically Relevant Emotional Distress and Functional Impairment in Children and Adolescents: Protocol for an Automated Speech Analysis Algorithm Development Study.

Even before the onset of the COVID-19 pandemic, children and adolescents were experiencing a mental health crisis, partly due to a lack of quality mental health services. The rate of suicide for Black youth has increased by 80%. By 2025, the health care system will be short of 225,000 therapists, further exacerbating the current crisis. Therefore, it is of utmost importance for providers, schools, youth mental health, and pediatric medical providers to integrate innovation in digital mental health to identify problems proactively and rapidly for effective collaboration with other health care providers. Such approaches can help identify robust, reproducible, and generalizable predictors and digital biomarkers of treatment response in psychiatry. Among the multitude of digital innovations to identify a biomarker for psychiatric diseases currently, as part of the macrolevel digital health transformation, speech stands out as an attractive candidate with features such as affordability, noninvasive, and nonintrusive. The protocol aims to develop speech-emotion recognition algorithms leveraging artificial intelligence/machine learning, which can establish a link between trauma, stress, and voice types, including disrupting speech-based characteristics, and detect clinically relevant emotional distress and functional impairments in children and adolescents. Informed by theoretical foundations (the Theory of Psychological Trauma Biomarkers and Archetypal Voice Categories), we developed our methodology to focus on 5 emotions: anger, happiness, fear, neutral, and sadness. Participants will be recruited from 2 local mental health centers that serve urban youths. Speech samples, along with responses to the Symptom and Functioning Severity Scale, Patient Health Questionnaire 9, and Adverse Childhood Experiences scales, will be collected using an Android mobile app. Our model development pipeline is informed by Gaussian mixture model (GMM), recurrent neural network, and long short-term memory. We tested our model with a public data set. The GMM with 128 clusters showed an evenly distributed accuracy across all 5 emotions. Using utterance-level features, GMM achieved an accuracy of 79.15% overall, while frame selection increased accuracy to 85.35%. This demonstrates that GMM is a robust model for emotion classification of all 5 emotions and that emotion frame selection enhances accuracy, which is significant for scientific evaluation. Recruitment and data collection for the study were initiated in August 2021 and are currently underway. The study results are likely to be available and published in 2024. This study contributes to the literature as it addresses the need for speech-focused digital health tools to detect clinically relevant emotional distress and functional impairments in children and adolescents. The preliminary results show that our algorithm has the potential to improve outcomes. The findings will contribute to the broader digital health transformation. DERR1-10.2196/46970.

Interpretable Emotion Classification Using Multidomain Feature of EEG Signals

Research on affective computing by physiological signals has become a new and important research branch of artificial intelligence. However, most of the research focuses only on the improvement of emotion classification models while neglecting the correlation between subjective emotional labels and implicit EEG feature information. In this study, we proposed an interpretable emotion classification framework based on food images as visual stimuli and a proposed EEG dataset acquired by a portable single-electrode EEG device. Then, we construct a multi-domain feature extraction method based on sliding time windows, which can effectively extract single-channel EEG features. Finally, we chose the extreme gradient boosting (XGBoost) model as our classifier in the proposed classification framework. The optimal accuracy of our single-channel EEG classification model was 94.76%, which reached the classification performance of multi-channel EEG classification. Based on XGBoost, we interpret the global and the local emotion feature selection method by calculating Shapley additive explanation (SHAP) values. Through the analysis of interpretable methods, we can not only obtain the contribution degree of features from three types of emotional labels (positive, negative, and neutral), but also know whether features have a positive or negative effect on classification results. The source codes of this work are publicly available at:https://github.com/VCMHE/MDF-EEG.

An Overview of IoT and ML-Based Emotion Recognition Systems

Abstract: Emotion recognition has emerged as a crucial research area in recent years due to its potential applications in various fields such as healthcare, marketing, and entertainment. In this study, we propose a novel approach to emotion recognition using the Internet of Things (IoT) and Machine Learning (ML) techniques. Our system consists of IoT devices equipped with sensors that capture physiological signals such as heart rate, skin conductance, and facial expressions. These signals are pre-processed and fed into an ML model for emotion classification. We evaluate our system on a publicly available dataset and achieve an accuracy of over 90%. Our results demonstrate the feasibility and effectiveness of using IoT and ML for emotion recognition, which can have significant implications for various industries. The proposed system can be extended to real-world applications such as personalized healthcare, customer feedback analysis, and content recommendation.

Research on Online Review of Ice and Snow Sports Based on Text Mining

Ice and snow economy, with the development of times and economy and the heat of 2022 Winter Olympic Games, ice and snow sports have gradually become a trend.However, domestic and foreign scholars to this emerging economy research is less, the level is shallow.This paper takes Ctrip platform as the data source, grabs the online review text of ice and snow sports, processes the data, quantifies the comment attribute of consumers by TF-IDF method, then uses LDA model to explore its potential theme, and finally constructs emotion classification model to analyze consumers 'emotion.The purpose is to understand their emotional bias and provide reference suggestions for national decision-making and ice and snow economic development.

Research on emotion classification technology of movie reviews based on topic attention mechanism and dual channel long short term memory.

In recent years, with the popularity of the Internet, more and more people like to comment on movies they have watched on the film platform after watching them. These reviews hide the reviewers' feedback on films. Mining the emotional orientation information in these reviews can provide consumers with shopping references and help businesses optimize film works and improve business strategies. Therefore, the emotional classification of film reviews has high research value because few emotion dictionaries and analysis tools are available for reference and use in film reviews. The accuracy of emotion classification still needs to be improved. This study introduces the attention mechanism and dual channel long short term memory (DC-LSTM) while building the emotion dictionary in the field of Chinese film review. It classifies Chinese film reviews in terms of topic-based fine-grained emotion. First, the emotion vector is constructed using the constructed movie review emotion lexicon. The semantic vector obtained by the Word2vector tool is input to LSTM to encode the comment text. Then, the topic attention module is used to decode. Finally, the final emotion classification result is obtained through the softmax function of the entire link layer and the output layer. The thematic attention modules constructed in this study are independent of each other for attention parameter adjustment and learning. One attention module corresponds to one film theme. In this study, eight themes, including "plot," "special effects," "original work," "music," "thought," "theme," "acting skills," and "joke," were extracted, and each theme was classified into three types of emotions: "positive," "neutral," and "negative." The experimental results on the crawled Chinese film review dataset show that the proposed algorithm is superior to some existing algorithms and models in accuracy, precision, recall and F1 measure. The DCLSTM based on the thematic attention mechanism (DCLSTM-TAM) model constructed in this study introduces the emotion vector into the network and adds the theme attention mechanism. It can not only classify the emotion for different topics of a film review but also effectively deal with film reviews with fuzzy emotional tendencies. It realizes the fine-grained emotion classification of film topics and improves the accuracy of emotion classification of film reviews. The emotion classification method and model proposed in this study have good transferability, and the change of training corpus is also applicable to other short text fields.

A systematic review on automated human emotion recognition using electroencephalogram signals and artificial intelligence

Brain-Computer Interaction (BCI) system intelligence has become more dependent on electroencephalogram (EEG)-based emotion recognition because of the numerous applications of emotion classification, such as recommender systems, cognitive load detection, etc. Emotion classification has drawn the recent buzz in Artificial Intelligence (AI)-powered research. In this article, we presented a systematic review of automated emotion recognition from EEG signals using AI. The review process is carried out based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA). After that EEG databases, and EEG preprocessing methods are included in this study. Also included feature extraction and feature selection methods. In addition, the included studies were divided into two types: i)deep learning(DL)-based emotion identification systems and ii) machine learning(ML)-based emotion classification models. The examined systems are analyzed based on their features, classification methodologies, classifiers, types of classified emotions, accuracy, and the datasets they employed. There is also an interesting comparison, a look at feature research trends, and ideas for new areas to study.

Exploring an Affective and Responsive Virtual Environment to Improve Remote Learning

Online classes are typically conducted by using video conferencing software such as Zoom, Microsoft Teams, and Google Meet. Research has identified drawbacks of online learning, such as “Zoom fatigue”, characterized by distractions and lack of engagement. This study presents the CUNY Affective and Responsive Virtual Environment (CARVE) Hub, a novel virtual reality hub that uses a facial emotion classification model to generate emojis for affective and informal responsive interaction in a 3D virtual classroom setting. A web-based machine learning model is employed for facial emotion classification, enabling students to communicate four basic emotions live through automated web camera capture in a virtual classroom without activating their cameras. The experiment is conducted in undergraduate classes on both Zoom and CARVE, and the results of a survey indicate that students have a positive perception of interactions in the proposed virtual classroom compared with Zoom. Correlations between automated emojis and interactions are also observed. This study discusses potential explanations for the improved interactions, including a decrease in pressure on students when they are not showing faces. In addition, video panels in traditional remote classrooms may be useful for communication but not for interaction. Students favor features in virtual reality, such as spatial audio and the ability to move around, with collaboration being identified as the most helpful feature.

Emotion Classification and Achievement of Students in Distance Learning Based on the Knowledge State Model

Since the outbreak of COVID-19, remote teaching methods have been widely adopted by schools. However, distance education can frequently lead to low student emotional engagement, which can not only cause learning burnout, but also weaken students’ interest in online learning. In view of the above problems, this study first proposed a learner knowledge state model that integrates learning emotions under the background of digital teaching to accurately describe the current learning state of students. Then, on the basis of the public face dataset lapa, we built an online multi-dimensional emotion classification model for students based on ResNet 18 neural network. Experiments showed that the method has an average recognition accuracy of 88.76% for the four cognitive emotions of joy, concentration, confusion, and boredom, among which the accuracy of joy and boredom is the highest, reaching 96.3% and 97.0% respectively. Finally, we analyzed the correlation between students’ emotional classification and grades in distance learning, and verified the effectiveness of the student’s emotional classification model in distance learning applications. In the context of digital teaching, this study provides technical support for distance learning emotion classification and learning early warning, and is of great significance to help teachers understand students’ emotional states in distance learning and promote students’ deep participation in the distance learning process.

Emotional speech-based personality prediction using NPSO architecture in deep learning

Speech is an effective way for analyzing mental and psychological health of a speaker's. Automatic speech recognition has been efficiently investigated for human-computer interaction and understanding the emotional & psychological anatomy of human behavior. Emotions and personality are studied to have a strong link while analyzing the prosodic speech parameters. The work proposes a novel personality and emotion classification model using PSO (particle swarm optimization) based CNN (convolution neural network): (NPSO) that predicts both (emotion and personality) The model is computationally efficient and outperforms language models. Cepstral speech features MFCC (mel frequency cepstral constants) is used to predict emotions with 90% testing accuracy and personality with 91% accuracy on SAVEE(Surrey Audio-Visual Expressed Emotion) individually. The correlation between emotion and personality is identified in the work. The experiment uses the four corpora SAVEE, RAVDESS (Ryerson Audio-Visual Database of Emotional Speech and Song), CREMAD (Crowd-sourced Emotional Multimodal Actors Dataset, TESS (Toronto emotional speech set) corpus, and the big five personality model for finding associations among emotions and personality traits. Experimental results show that the classification accuracy scores for combined datasets are 74% for emotions and 89% for Personality classifications. The proposed model works on seven emotions and five classes of personality. Results prove that MFCC is enough effective in characterizing and recognizing emotions and personality simultaneously.

An Analysis of the Evolution of Online Public Opinion on Public Health Emergencies by Combining CNN-BiLSTM + Attention and LDA

In this paper, the Foxconn epidemic event in Zhengzhou was taken as an example to analyze the evolution of online public opinion on public health emergencies. In order to improve the performance of online public opinion analysis, based on the life cycle theory and LDA theory, the emotional changes of Internet users in four stages of the Foxconn incident centered on the evolution of inscription were divided. The emotions of netizen speech at different stages are analyzed based on CNN-BiLSTM + Attention model, which uses Convolutional Neural Network (CNN) to extract local features. Bi-directional Long Short-Term Memory (BiLSTM) is used to efficiently extract contextual semantic features and long distance dependencies, and then combined with attention mechanism to add emotional features. Finally, Softmax classifier realizes text emotion prediction. The experimental results show that: compared with TextCNN, BiLSTM, BiLSTM + Attenion, CNN-BiLSTM model, the emotion classification model has better effects in the accuracy rate, accuracy rate, recall rate and F value. By analyzing the emotional distribution and evolution trend of public opinion under “text topic”, the paper accurately deconstructs the development characteristics of public opinion in public health emergencies, in order to provide reference for relevant departments to deal with public opinion in public health emergencies.