Emotion Analysis Model Research Articles

The geopolitics of vaccine media representation in Orbán’s Hungary—an AI-supported sentiment analysis

Extant studies on the European media coverage of the COVID-19 pandemic generally posit a linear relationship between the severity of the public health emergency and the volume of media reports. However, domestic politics and geopolitics may also impact the saliency, distribution, and sentiment of coverage in different outlets. Under Viktor Orbán’s illiberal leadership, Hungary sought deals for ventilators and vaccines from China and Russia—a deviation from joint European procurements. In this article, we conduct a content analysis of pro-government and Orbán-critical media to examine differences in their treatment of Eastern and Western vaccines. We relied on state-of-the-art deep learning analysis (a branch of articifial intelligence) to investigate all COVID-19-related articles (N = 72,339) published on three major Hungarian news portals between March 2020 and March 2022. We used a new fine-tuned BERT model for emotion analysis, the categories of which have been aggregated into three sentiment labels (positive, negative, and neutral). Our sentiment analysis results show a positive correlation between the number of sentences mentioning at least one of the vaccines and the (first) shots administered for only one outlet out of three. The pro-government portal in the sample showed more positivity towards Western vaccines than a hard-right, anti-government one. This latter also produced more positive reports concerning the Russian vaccine. These results shed light on the complex geopolitics of vaccine narratives in Hungarian media. Our research contributes not only to our understanding of illiberal media systems but also by sharing a new public dataset and a fine-tuned large language model that is applicable to alternative research questions and designs.

DGFN Multimodal Emotion Analysis Model Based on Dynamic Graph Fusion Network

In recent years, integrating text and image data for sentiment analysis in social networks has become a key approach. However, techniques for capturing complex cross-modal information and effectively fusing multimodal features still have shortcomings. We design a multimodal sentiment analysis model called the Dynamic Graph-Text Fusion Network (DGFN) to address these challenges. Text features are captured by leveraging the neighborhood information aggregation properties of Graph Convolutional Networks, treating words as nodes and integrating their features through their adjacency relationships. Additionally, the multi-head attention mechanism is utilized to extract rich semantic information from different subspaces simultaneously. For image feature extraction, a convolutional attention module is employed. Subsequently, an attention-based fusion module integrates the text and image features. Experimental results on the two datasets show significant improvements in sentiment classification accuracy and F1 scores, validating the effectiveness of the proposed DGFN model.

RBL: A Multimodal Emotion Analysis Model Based on Language Guided Transformer

Emotional analysis has important value in the economic and social fields such as public opinion analysis, market research and risk prediction. Nowadays, the rapid development of short video media has led to the expansion of emotional analysis content from text content to multimedia content. Multimodal emotion analysis is to mine emotional information from the fusion of text, language and pictures. At present, some achievements have been made in the research of emotion analysis for multimodal data. However, due to the huge content of multimodal information, contextual emotion correlation between modes and within modes, the current multimodal emotion analysis still has some shortcomings. In view of this, this study proposes the RBL model - a multimodal sentiment analysis model based on LGT (Linguistics-Guided Transformer), which uses Bert and Resnet models to extract text features and image features, and Librosa toolkit to extract voice features.

Part of speech weighted multi-modal emotion analysis model with dynamic adjustment of semantic representation

Part of speech weighted multi-modal emotion analysis model with dynamic adjustment of semantic representation

감정 어휘 사전을 이용한 문학 감정분류

Recently, emotion analysis technology has been developed to classify emotions felt in texts such as sentences and documents, and it is being used in a variety of research, including literature. Because emotions are an important foundation for understanding literature, it is important to apply emotion analysis technology in literary research, and efforts are needed to improve problems that arise during the emotion analysis process for the development of technology. This study explains the problems that arise from existing labeling methods when constructing learning data, and introduces ‘emotion lexicon-based emotion labeling’ to improve these problems. To prove the effectiveness of emotion lexicon-based labeling, we constructed two learning data with different emotion labeling methods, entered them into a emotion analysis model, and compared the results. As a result, the accuracy of the model using emotion lexicon-based labeling was 91.95%, showing approximately 20% higher performance than the model using existing labeling. In addition, as a result of going beyond quantitative evaluation and directly checking the analysis results by inputting literary passages from the CSAT into the model, the model using emotion lexicon-based labeling showed better predictive power.

Investigation of Imbalanced Sentiment Analysis in Voice Data: A Comparative Study of Machine Learning Algorithms

INTRODUCTION: Language serves as the primary conduit for human expression, extending its reach into various communication mediums like email and text messaging, where emoticons are frequently employed to convey nuanced emotions. In the digital landscape of long-distance communication, the detection and analysis of emotions assume paramount importance. However, this task is inherently challenging due to the subjectivity inherent in emotions, lacking a universal consensus for quantification or categorization.OBJECTIVES: This research proposes a novel speech recognition model for emotion analysis, leveraging diverse machine learning techniques along with a three-layer feature extraction approach. This research will also through light on the robustness of models on balanced and imbalanced datasets. METHODS: The proposed three-layered feature extractor uses chroma, MFCC, and Mel method, and passes these features to classifiers like K-Nearest Neighbour, Gradient Boosting, Multi-Layer Perceptron, and Random Forest.RESULTS: Among the classifiers in the framework, Multi-Layer Perceptron (MLP) emerges as the top-performing model, showcasing remarkable accuracies of 99.64%, 99.43%, and 99.31% in the Balanced TESS Dataset, Imbalanced TESS (Half) Dataset, and Imbalanced TESS (Quarter) Dataset, respectively. K-Nearest Neighbour (KNN) follows closely as the second-best classifier, surpassing MLP's accuracy only in the Imbalanced TESS (Half) Dataset at 99.52%.CONCLUSION: This research contributes valuable insights into effective emotion recognition through speech, shedding light on the nuances of classification in imbalanced datasets.

Emotional analysis of joint sports quality expansion tasks based on multi-modal feature fusion

A multi-modal feature based motion emotion analysis model based on a fusion deep learning model is proposed for the problem of analyzing the motion emotions of participants in the joint exercise quality expansion task. This model involves three major modalities: EEG signals, peripheral physiological signals, and facial expression signals, and processes and fuses the information of these three main modalities to achieve the effect of processing multi-dimensional motor emotional information. At the same time, this study introduces the design concept of residual networks, using self attention modules and multi head mutual attention modules to process different modal features. The results showed that the combination of peripheral physiological modality and facial expression modality had the highest accuracy among the three modality combinations, with an accuracy rate of 88.8 %. The feature fusion method based on the cascaded residual attention mechanism module has better accuracy and F1 Score performance than other methods. In addition, different emotional states can be effectively identified and distinguished in these three modalities, indicating that the model has a wide range of possibilities in practical applications.

Analysis of Pharmaceutical Companies' Social Media Activity during the COVID-19 Pandemic and Its Impact on the Public.

The COVID-19 pandemic, a period of great turmoil, was coupled with the emergence of an "infodemic", a state when the public was bombarded with vast amounts of unverified information from dubious sources that led to a chaotic information landscape. The excessive flow of messages to citizens, combined with the justified fear and uncertainty imposed by the unknown virus, cast a shadow on the credibility of even well-intentioned sources and affected the emotional state of the public. Several studies highlighted the mental toll this environment took on citizens by analyzing their discourse on online social networks (OSNs). In this study, we focus on the activity of prominent pharmaceutical companies on Twitter, currently known as X, as well as the public's response during the COVID-19 pandemic. Communication between companies and users is examined and compared in two discrete channels, the COVID-19 and the non-COVID-19 channel, based on the content of the posts circulated in them in the period between March 2020 and September 2022, while the emotional profile of the content is outlined through a state-of-the-art emotion analysis model. Our findings indicate significantly increased activity in the COVID-19 channel compared to the non-COVID-19 channel while the predominant emotion in both channels is joy. However, the COVID-19 channel exhibited an upward trend in the circulation of fear by the public. The quotes and replies produced by the users, with a stark presence of negative charge and diffusion indicators, reveal the public's preference for promoting tweets conveying an emotional charge, such as fear, surprise, and joy. The findings of this research study can inform the development of communication strategies based on emotion-aware messages in future crises.

Constructing an Innovative Interactive Experience of Stage Visual Design for Augmented Reality in Costume Performance

Abstract Augmented reality (AR) technology has rapidly advanced across various domains, propelled by its robust interactive immersion and the seamless integration of real and virtual environments. However, its exploration and deployment in theatrical contexts remain limited. This study leverages the Kinect system to capture images during costume performances, employing algorithms for dynamic frame difference merging and human-computer interaction to detect performers’ body movements. Building on this, the study constructs a visually innovative stage for costume performances that enhances the interactive experience for the audience. Additionally, a multimodal emotion analysis model is utilized to assess audience emotions, demonstrating significantly higher accuracy and F1 scores compared to other emotion analysis models. This model effectively integrates speech, expression, and action, surpassing the performance of unimodal analyses in emotion recognition. Furthermore, the audience's experiential perception of stage lighting effects notably exceeds expectations (P=0.013 < 0.05), underscoring an enhanced interaction experience. This research substantiates the transformative potential of AR technology in stage design, offering audiences a more innovative visual and interactive experience, and serves as a valuable reference for future applications in this field.

Design of Emotion Analysis Model IABC-Deep Learning-based for Vocal Performance

Design of Emotion Analysis Model IABC-Deep Learning-based for Vocal Performance

Sentiment analysis of art and design works using deep learning

Abstract Deep learning technology has the characteristics of automatic learning, nonlinear modeling, etc., which makes the machine algorithm better adapt to the data so as to better complete the task of sentiment analysis. The purpose of this paper is to explore the application of deep learning technology in the emotional analysis of art design works. After combining the emotion construction mechanism of art design works, based on the superior performance of convolutional neural network image recognition, it is introduced into the emotion analysis of art design works, combined with the attention mechanism model, to construct the emotion analysis model of art design works based on deep learning. Art design works are collected as the experimental dataset. The emotion analysis effect of this paper’s model for art design works is evaluated through the comparison of emotion classification indexes. The comparison of ROC curves of different models, and the emotion instance analysis of art design works is carried out. The experiments show that the experimental accuracy, precision, recall, and F1 value of this paper’s model range from 81% to 86%, and the F1 and ROC values are improved by 2.3% to 7.0% and 1.7% to 4.2% compared with the comparison model, which reflects the high accuracy and precision of this paper’s model for the sentiment analysis of art design works. Seven emotional patterns were found in tea art design works, and the model can be used to analyze the emotions of art design works.

Strategies for Monitoring and Managing Online Public Opinion in Universities Under the Background of Big Data

<p>In response to the issue of some keywords not being logged in or having inaccurate semantics in current public opinion monitoring, this article uses an improved word segmentation method to extract semantic features. In response to the current issue of unable to control the emotional direction of public opinion comments in public opinion analysis, an emotion analysis model Bi_GRU is proposed for sentiment analysis. Finally, using students’ commonly used Weibo as a verification scenario, sensitive information such as “food safety” and “campus bullying” is screened to control the emotional direction of college students. The final proof is that the method proposed in this article can effectively supervise public opinion in a centralized environment and provide effective means for student management. </p> <p> </p>

Using cognitive mapping to analyze the aesthetic value of music education in colleges and universities

Abstract In this paper, firstly, on the basis of cognitive mapping, music data are processed in four directions, namely, sound spectrum extraction, audio superposition, audio tempo, and tone intensity adjustment, and then audio sequences are sliced and diced, so that the cognitive mapping can be more focused on capturing localized useful information. Then, the BERT model is used to encode the music sequences so that the output music sequences already contain aesthetic and emotional features and the cognitive mapping-based music emotion analysis model is constructed according to the music emotion learning layer, the emotion feature aggregation layer, and the full connectivity layer, and analyzes the aesthetic value of music education in colleges and universities. According to the results, the BERT model had a difference of 0.13% on the MED dataset and 0.03% on the MTD dataset. On the mean value of the aesthetic value of traditional music, introverted personality (3.61) was higher than extroverted personality (3.52). This study has a positive effect on the cultivation of aesthetic value in music education in colleges and universities and proposes effective ways to enhance aesthetic value in college and university music teaching.

Speech Emotion Analysis of Short English Readings based on the CAM-SPAT Model

With the development of technology, voice sentiment analysis has also undergone rapid development, and its application fields are constantly expanding. Multimodal models have become a key focus of researchers due to their ability to better predict emotions. In order to help English learners improve their oral English proficiency, a deep learning based emotional analysis model for English short text reading is proposed, and this model is used to analyze emotions in English reading. Additionally, a cross-modal attention mechanism based on a prediction-assisted task was developed to identify emotions in English reading aloud in state and a two-layer attention-based bi-directional long- and short-term memory network was created to classify emotions in English reading aloud. The results of the research revealed that the classification model’s mean F1 value was 98.54%, the detection model’s mean F1 value was 85.13%, and the speech emotion analysis model’s mean F1 value was 73.55, which was not significantly different from the mean of the professionals’ ratings. The significance of the study lies in providing English learners with a method and pathway to improve their oral English proficiency.

AN EMOTION RECOGNITION MODEL BASED ON LONG SHORT-TERM MEMORY NETWORKS AND EEG SIGNALS AND ITS APPLICATION IN PARAMETRIC DESIGN

One of the design objectives of a product is to create a positive emotional user experience. Through careful design, the product can evoke emotional resonance in users and stimulate their pleasure and satisfaction. Therefore, emotion recognition is crucial for parameterized product design. Considering that emotion recognition based on electroencephalogram (EEG) signals is more objective and accurate compared to methods such as text and surveys, this paper proposes an emotion analysis model based on long short-term memory (LSTM) and EEG and applies it to parameterized design. The main contributions of this paper are as follows. (1) Constructing a high-accuracy emotion recognition model. First, EEG data reflecting the characteristic patterns of brain activities in different emotional states are collected through EEG electrodes. Then, the EEG data are input into the LSTM network for training, enabling it to learn and capture the features associated with emotional states. During the training process, the model learns to extract crucial emotional features from the EEG data for emotion state recognition. This model can automatically learn emotional features, handle long-term dependencies and provide a more accurate and reliable solution for emotion recognition tasks. (2) Creating an EEG dataset specifically for evaluating emotions related to a product and using the trained emotion recognition model to classify this dataset, obtaining emotion classification results. The emotion classification results can be used to determine which parameter designs in product development need to be retained or discarded. These parameter designs can involve aspects such as user experience, functionality, aesthetics, usability and user-friendliness. Decisions can be made based on the emotion classification results to improve the quality and user satisfaction of the product.

Recognition and Analysis of Scene-Emotion in Photographic Works Based on AI Technology

Emotional effect is highly subjective in people's cognitive process, and a single discrete emotional feeling can hardly support the description of the immersion scene, which also puts forward higher requirements for emotional calculation in photography. Therefore, this article first constructs a photographic scene recognition model, and then establishes a visual emotion analysis model which optimizes the basic structure of vgg19 through CNN, extracts the user's photography situation information from the corresponding image metadata, establishes the mapping relationship between situation and emotion, and obtains the low-dimensional dense vector representation of the situation features through embedding. The authors divided eight emotional categories; accuracy of the model is compared and the feature distribution of scene-emotion in different works is analyzed. The results show that the accuracy of the scene-emotion recognition model of photographic works after multimodal fusion is high, reaching 73.9%, in addition, different shooting scenes can distinguish the emotional characteristics of works.

Multimodal Emotion Analysis Model based on Interactive Attention Mechanism

In traditional multi-modal sentiment analysis, feature fusion is usually achieved by simple splicing, and multi-modal sentiment analysis is only trained as a single task, without considering the contribution of inter-modal information interaction to sentiment analysis and the correlation and constraint relationship between multi-modal and single-modal (text, video and audio) tasks. Therefore, a multi-task model based on interactive attention mechanism is proposed in this paper, which uses inter-modal attention mechanism and single-modal self-attention mechanism to train multi-modal sentiment analysis and single-modal sentiment analysis together, so as to make full use of inter-modal and inter-task information sharing, mutual complement, and reduce noise to improve the overall recognition performance. Experiments show that the proposed model performs well on MOSI and MOSEI common data sets for multimodal sentiment analysis.

User Sentiment Analysis of COVID-19 via Adversarial Training Based on the BERT-FGM-BiGRU Model

With the rapid development of social network platforms, Sina Weibo has become the main carrier for modern netizens to express public views and emotions. How to obtain the tendency of public opinion and analyze the text’s emotion more accurately and reasonably has become one of the main challenges for the government to monitor public opinion in the future. Due to the sparseness of Weibo text data and the complex semantics of Chinese, this paper proposes an emotion analysis model based on the Bidirectional Encoder Representation from Transformers pre-training model (BERT), Fast Gradient Method (FGM) and the bidirectional Gated Recurrent Unit (BiGRU), namely BERT-FGM-BiGRU model. Aiming to solve the problem of text polysemy and improve the extraction effect and classification ability of text features, this paper adopts the BERT pre-training model for word vector representation and BiGRU for text feature extraction. In order to improve the generalization ability of the model, this paper uses the FGM adversarial training algorithm to perturb the data. Therefore, a BERT-FGM-BiGRU model is constructed with the goal of sentiment analysis. This paper takes the Chinese text data from the Sina Weibo platform during COVID-19 as the research object. By comparing the BERT-FGM-BiGRU model with the traditional model, and combining the temporal and spatial characteristics, it further studies the changing trend of user sentiment. Finally, the results show that the BERT-FGM-BiGRU model has the best classification effect and the highest accuracy compared with other models, which provides a scientific method for government departments to supervise public opinion. Based on the classification results of this model and combined with the temporal and spatial characteristics, it can be found that public sentiment is spatially closely related to the severity of the pandemic. Due to the imbalance of information sources, the public showed negative emotions of fear and worry in the early and middle stages, while in the later stage, the public sentiment gradually changed from negative to positive and hopeful with the improvement of the epidemic situation.

Retracted: Emotion Analysis Model of Microblog Comment Text Based on CNN-BiLSTM.

[This retracts the article DOI: 10.1155/2022/1669569.].

Multilingual Speech Emotion Research Based on Data Mining

In order to improve the effect of speech emotion research, this paper combines data mining technology to construct a speech emotion research model and establishes a state space model of speech emotion risk problem. Moreover, this paper uses fuzzy variables to describe the external uncertain environmental factors and intuitively shows the relationship between each technology, that is, clearly shows how actions and external environmental factors affect their action results, which is helpful in understanding the voice emotional risk problem from the root. In addition, this paper uses the basic knowledge of credibility theory to transform the model and finally transform the model into an optimal control problem. Through the speech emotion simulation experiment, it can be seen that the multilingual speech emotion analysis model based on data mining proposed in this paper has a good effect in speech emotion analysis.