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Electronics
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New Advances in Affective Computing
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New Advances in Affective Computing

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 15 September 2024 | Viewed by 6131

Special Issue Editors

Prof. Dr. Ruifeng Xu

E-Mail Website
Guest Editor
School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen 518055, China
Interests: affective computing; sentiment analysis; argumentation mining
Prof. Dr. Tong Xu

E-Mail Website
Guest Editor
Lab of Big Data Analysis and Application, University of Science and Technology of China, Hefei 230027, China
Interests: natural language processing; social media analysis; multimodal intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Yanyan Zhao

E-Mail Website
Guest Editor
Faculty of Computing, Harbin Institute of Technology, Harbin 150001, China
Interests: natural language processing; sentiment analysis

Special Issue Information

Dear Colleagues,

The special issue of New Advances in Affective Computing aims to explore the cutting-edge developments and emerging trends in the field of affective computing. Affective computing focuses on the study and development of intelligent systems that can recognize, interpret, and respond to human sentiments. This Special Issue aims to provide a platform for researchers, academicians, and industry professionals to showcase their novel contributions in the realm of affective computing. The scope of this Special Issue encompasses various interdisciplinary areas, including computer science, artificial intelligence, psychology, neuroscience, and human-computer interaction. Topics of interest include but are not limited to sentiment recognition and generation, affective interaction design, affective computing in social media and big data, affective robotics, and ethical considerations in affective computing. Particularly, this special issue emphasizes the development of multi-modal sentiment analysis, aiming to explore how sentimental information can be extracted from multiple sources such as text, images, video, audio, and more, and integrated to better understand and interpret human sentiments accurately. Additionally, this special issue investigates the research related to stance detection and argumentation mining, which contributes to a deeper understanding of individual and societal sentimental attitudes. By presenting the latest advancements and discoveries in affective computing, this special issue intends to foster collaborations, inspire new research directions, and pave the way for the practical application of affective computing technologies in diverse domains.

Prof. Dr. Ruifeng Xu
Prof. Dr. Tong Xu
Dr. Yanyan Zhao
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • affective computing
  • sentiment analysis
  • multi-modal sentiment analysis
  • stance detection
  • argumentation mining

Published Papers (6 papers)

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Research

17 pages, 295 KiB  
Article
Single- and Cross-Lingual Speech Emotion Recognition Based on WavLM Domain Emotion Embedding
by Jichen Yang, Jiahao Liu, Kai Huang, Jiaqi Xia, Zhengyu Zhu and Han Zhang
Electronics 2024, 13(7), 1380; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics13071380 - 05 Apr 2024
Viewed by 416
Abstract
Unlike previous approaches in speech emotion recognition (SER), which typically extract emotion embeddings from a trained classifier consisting of fully connected layers and training data without considering contextual information, this research introduces a novel approach. It integrates contextual information into the feature extraction [...] Read more.
Unlike previous approaches in speech emotion recognition (SER), which typically extract emotion embeddings from a trained classifier consisting of fully connected layers and training data without considering contextual information, this research introduces a novel approach. It integrates contextual information into the feature extraction process. The proposed approach is based on the WavLM representation and incorporates a contextual transform, along with fully connected layers, training data, and corresponding label information, to extract single-lingual WavLM domain emotion embeddings (SL-WDEEs) and cross-lingual WavLM domain emotion embeddings (CL-WDEEs) for single-lingual and cross-lingual SER, respectively. To extract CL-WDEEs, multi-task learning is employed to remove language information, marking it as the first work to extract emotion embeddings for cross-lingual SER. Experimental results on the IEMOCAP database demonstrate that the proposed SL-WDEE outperforms some commonly used features and known systems, while results on the ESD database indicate that the proposed CL-WDEE effectively recognizes cross-lingual emotions and outperforms many commonly used features. Full article
(This article belongs to the Special Issue New Advances in Affective Computing)
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19 pages, 868 KiB  
Article
Combining wav2vec 2.0 Fine-Tuning and ConLearnNet for Speech Emotion Recognition
by Chenjing Sun, Yi Zhou, Xin Huang, Jichen Yang and Xianhua Hou
Electronics 2024, 13(6), 1103; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics13061103 - 17 Mar 2024
Viewed by 629
Abstract
Speech emotion recognition poses challenges due to the varied expression of emotions through intonation and speech rate. In order to reduce the loss of emotional information during the recognition process and to enhance the extraction and classification of speech emotions and thus improve [...] Read more.
Speech emotion recognition poses challenges due to the varied expression of emotions through intonation and speech rate. In order to reduce the loss of emotional information during the recognition process and to enhance the extraction and classification of speech emotions and thus improve the ability of speech emotion recognition, we propose a novel approach in two folds. Firstly, a feed-forward network with skip connections (SCFFN) is introduced to fine-tune wav2vec 2.0 and extract emotion embeddings. Subsequently, ConLearnNet is employed for emotion classification. ConLearnNet comprises three steps: feature learning, contrastive learning, and classification. Feature learning transforms the input, while contrastive learning encourages similar representations for samples from the same category and discriminative representations for different categories. Experimental results on the IEMOCAP and the EMO-DB datasets demonstrate the superiority of our proposed method compared to state-of-the-art systems. We achieve a WA and UAR of 72.86% and 72.85% on IEMOCAP, and 97.20% and 96.41% on the EMO-DB, respectively. Full article
(This article belongs to the Special Issue New Advances in Affective Computing)
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12 pages, 990 KiB  
Article
Multi-Modal Sarcasm Detection with Sentiment Word Embedding
by Hao Fu, Hao Liu, Hongling Wang, Linyan Xu, Jiali Lin and Dazhi Jiang
Electronics 2024, 13(5), 855; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics13050855 - 23 Feb 2024
Cited by 2 | Viewed by 1007
Abstract
Sarcasm poses a significant challenge for detection due to its unique linguistic phenomenon where the intended meaning is often opposite of the literal expression. Current sarcasm detection technology primarily utilizes multi-modal processing, but the connotative semantic information provided by the modality itself is [...] Read more.
Sarcasm poses a significant challenge for detection due to its unique linguistic phenomenon where the intended meaning is often opposite of the literal expression. Current sarcasm detection technology primarily utilizes multi-modal processing, but the connotative semantic information provided by the modality itself is limited. It is a challenge to mine the semantic information contained in the combination of sarcasm samples and external commonsense knowledge. Furthermore, as the essence of sarcasm detection lies in measuring emotional inconsistency, the rich semantic information may introduce excessive noise to inconsistency measurement. To mitigate these limitations, we propose a hierarchical framework in this paper. Specifically, to enrich the semantic information of each modality, our approach uses sentiment dictionaries to obtain the sentiment vectors by evaluating the words extracted from various modalities, and then combines them with each modality. Furthermore, in order to mine the joint semantic information implied in the modalities and improve measurement of emotional inconsistency, the emotional information representation obtained by fusing each modality’s data is concatenated with the sentiment vector. Then, cross-modal fusion is performed through cross-attention, and, finally, the sarcasm is recognized by fusing low-level information in the cross-modal fusion layer. Our model is evaluated on a public multi-modal sarcasm detection dataset based on Twitter, and the results demonstrate its superiority. Full article
(This article belongs to the Special Issue New Advances in Affective Computing)
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18 pages, 718 KiB  
Article
Construction of an Event Knowledge Graph Based on a Dynamic Resource Scheduling Optimization Algorithm and Semantic Graph Convolutional Neural Networks
by Xing Liu, Long Zhang, Qiusheng Zheng, Fupeng Wei, Kezheng Wang, Zheng Zhang, Ziwei Chen, Liyue Niu and Jizong Liu
Electronics 2024, 13(1), 11; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics13010011 - 19 Dec 2023
Cited by 1 | Viewed by 967
Abstract
Presently, road and traffic control construction on most university campuses cannot keep up with the growth of the universities. Campus roads are not very wide, crossings do not have lights, and there are no full-time traffic management personnel. Teachers and students are prone [...] Read more.
Presently, road and traffic control construction on most university campuses cannot keep up with the growth of the universities. Campus roads are not very wide, crossings do not have lights, and there are no full-time traffic management personnel. Teachers and students are prone to forming a peak flow of people when going to and from classes. This has led to a constant stream of traffic accidents. It is critical to conduct a comprehensive analysis of this issue by utilizing voluminous data pertaining to school traffic incidents in order to safeguard the lives of faculty and students. In the case of domestic universities, fewer studies have studied knowledge graph construction methods for traffic safety incidents. In event knowledge graph construction, the reasonable release and recycling of computational resources are inefficient, and existing entity–relationship joint extraction methods are unable to deal with ternary overlapping and entity boundary ambiguity problems in relationship extraction. In response to the above problems, this paper proposes a knowledge graph construction method for university on-campus traffic safety events with improved dynamic resource scheduling algorithms and multi-layer semantic graph convolutional neural networks. The experiment’s results show that the proposed dynamic computational resource scheduling method increases GPU and CPU use by 25% and 9%. On the public dataset, the proposed data extraction model’s F1 scores for event triples increase by 1.3% on the NYT dataset and by 0.4% on the WebNLG dataset. This method can help the relevant university personnel in dealing with unexpected traffic incidents and reduce the impact on public opinion. Full article
(This article belongs to the Special Issue New Advances in Affective Computing)
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20 pages, 927 KiB  
Article
A Dynamic Emotional Propagation Model over Time for Competitive Environments
by Zhihao Chen, Bingbing Xu, Tiecheng Cai, Zhou Yang and Xiangwen Liao
Electronics 2023, 12(24), 4937; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics12244937 - 08 Dec 2023
Viewed by 840
Abstract
Emotional propagation research aims to discover and show the laws of opinion evolution in social networks. The short-term observation of the emotional propagation process for a predetermined time window ignores situations in which users with different emotions compete over a long diffusion time. [...] Read more.
Emotional propagation research aims to discover and show the laws of opinion evolution in social networks. The short-term observation of the emotional propagation process for a predetermined time window ignores situations in which users with different emotions compete over a long diffusion time. To that end, we propose a dynamic emotional propagation model based on an independent cascade. The proposed model is inspired by the interpretable factors of the reinforced Poisson process, portraying the “rich-get-richer” phenomenon within a social network. Specifically, we introduce a time-decay mechanism to illustrate the change in influence over time. Meanwhile, we propose an emotion-exciting mechanism allowing prior users to affect the emotions of subsequent users. Finally, we conduct experiments on an artificial network and two real-world datasets—Wiki, with 7194 nodes, and Bitcoin-OTC, with 5881 nodes—to verify the effectiveness of our proposed model. The proposed method improved the F1-score by 3.5% and decreased the MAPE by 0.059 on the Wiki dataset. And the F1-score improved by 0.4% and the MAPE decreased by 0.013 on the Bitcoin-OTC dataset. In addition, the experimental results indicate a phenomenon of emotions in social networks tending to converge under the influence of opinion leaders after a long enough time. Full article
(This article belongs to the Special Issue New Advances in Affective Computing)
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Graphical abstract

23 pages, 6708 KiB  
Article
Deep Learning Short Text Sentiment Analysis Based on Improved Particle Swarm Optimization
by Yaowei Yue, Yun Peng and Duancheng Wang
Electronics 2023, 12(19), 4119; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics12194119 - 02 Oct 2023
Cited by 3 | Viewed by 995
Abstract
Manually tuning the hyperparameters of a deep learning model is not only a time-consuming and labor-intensive process, but it can also easily lead to issues like overfitting or underfitting, hindering the model’s full convergence. To address this challenge, we present a BiLSTM-TCSA model [...] Read more.
Manually tuning the hyperparameters of a deep learning model is not only a time-consuming and labor-intensive process, but it can also easily lead to issues like overfitting or underfitting, hindering the model’s full convergence. To address this challenge, we present a BiLSTM-TCSA model (BiLSTM combine TextCNN and Self-Attention) for deep learning-based sentiment analysis of short texts, utilizing an improved particle swarm optimization (IPSO). This approach mimics the global random search behavior observed in bird foraging, allowing for adaptive optimization of model hyperparameters. In this methodology, an initial step involves employing a Generative Adversarial Network (GAN) mechanism to generate a substantial corpus of perturbed text, augmenting the model’s resilience to disturbances. Subsequently, global semantic insights are extracted through Bidirectional Long Short Term Memory networks (BiLSTM) processing. Leveraging Convolutional Neural Networks for Text (TextCNN) with diverse convolution kernel sizes enables the extraction of localized features, which are then concatenated to construct multi-scale feature vectors. Concluding the process, feature vector refinement and the classification task are accomplished through the integration of Self-Attention and Softmax layers. Empirical results underscore the effectiveness of the proposed approach in sentiment analysis tasks involving succinct texts containing limited information. Across four distinct datasets, our method attains impressive accuracy rates of 91.38%, 91.74%, 85.49%, and 94.59%, respectively. This performance constitutes a notable advancement when compared against conventional deep learning models and baseline approaches. Full article
(This article belongs to the Special Issue New Advances in Affective Computing)
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