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Future Internet
Special Issues
Affective Computing and Sentiment Analysis
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Affective Computing and Sentiment Analysis

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Techno-Social Smart Systems".

Deadline for manuscript submissions: closed (31 July 2022) | Viewed by 10895

Special Issue Editors

Prof. Dr. Haoran Xie

E-Mail Website
Guest Editor
Department of Computing and Decision Sciences, Lingnan University, Hong Kong
Interests: artificial intelligence in education, affective computing, digital humanities, and educational data mining
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Gary Cheng

E-Mail Website
Guest Editor
Department of Mathematics and Information Technology, The Education University of Hong Kong, 10 Lo Ping Road, Tai Po, New Territories, Hong Kong 999077, China
Interests: artificial intelligence in education; information technology supported L2 learning; ePortfolio-mediated learning; computer programming
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Fu-Lee Wang

E-Mail Website
Guest Editor
School of Science and Technology, Hong Kong Metropolitan University, Hong Kong, China
Interests: blended learning; educational technology; applied algorithm; information retrieval; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Emotions are one of the most complicated features of human intelligence. How to model and represent emotions in a computational way is one of the key issues for understanding human intelligence and developing artificial general intelligence. With the rapid development of deep learning techniques in the field of artificial intelligence in recent years, machines are capable of having similar or even better performance than humans in specific tasks, including chess-playing, image classification, object recognition, and so on. However, it is still quite challenging for them to understand human emotions. The reasons can be categorized as follows. First, the notion of emotions is unclear and not well-defined, and there have been many emotional/affective models in psychology. Second, the data for affective computing models are quite vague and noisy. For example, the EEG signals for identifying human emotions or social media texts for understanding reader sentiments/emotions often contain many noises. Therefore, this Special Issue aims to capture recent advancements in affective computing and sentiment analysis. The topics interests are machine learning and deep learning models for affective computing and sentiment analysis applications, including but not limited to the following:

  • Deep neural networks for affective computing and sentiment analysis;
  • Traditional machine learning models for affective computing and sentiment analysis;
  • Affective computing and sentiment analysis in natural language processing;
  • Affective computing and sentiment analysis in financial data mining;
  • Affective computing and sentiment analysis in biosignal data mining; 
  • Emotion/sentiment representation models;
  • Affective computing and sentiment analysis in social media or social network analysis.

Prof. Dr. Haoran Xie
Prof. Dr. Gary Cheng
Prof. Dr. Fu Lee Wang
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. Future Internet is an international peer-reviewed open access monthly 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 1600 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

  • emotion classification
  • affective computing
  • sentiment analysis

Published Papers (4 papers)

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Research

19 pages, 662 KiB  
Article
Microblog Sentiment Analysis Based on Dynamic Character-Level and Word-Level Features and Multi-Head Self-Attention Pooling
by Shangyi Yan, Jingya Wang and Zhiqiang Song
Future Internet 2022, 14(8), 234; https://doi.org/10.3390/fi14080234 - 29 Jul 2022
Cited by 4 | Viewed by 1604
Abstract
To address the shortcomings of existing deep learning models and the characteristics of microblog speech, we propose the DCCMM model to improve the effectiveness of microblog sentiment analysis. The model employs WOBERT Plus and ALBERT to dynamically encode character-level text and word-level text, [...] Read more.
To address the shortcomings of existing deep learning models and the characteristics of microblog speech, we propose the DCCMM model to improve the effectiveness of microblog sentiment analysis. The model employs WOBERT Plus and ALBERT to dynamically encode character-level text and word-level text, respectively. Then, a convolution operation is used to extract local key features, while cross-channel feature fusion and multi-head self-attention pooling operations are used to extract global semantic information and filter out key data, before using the multi-granularity feature interaction fusion operation to effectively fuse character-level and word-level semantic information. Finally, the Softmax function is used to output the results. On the weibo_senti_100k dataset, the accuracy and F1 values of the DCCMM model improve by 0.84% and 1.01%, respectively, compared to the best-performing comparison model. On the SMP2020-EWECT dataset, the accuracy and F1 values of the DCCMM model improve by 1.22% and 1.80%, respectively, compared with the experimental results of the best-performing comparison model. The results showed that DCCMM outperforms existing advanced sentiment analysis models. Full article
(This article belongs to the Special Issue Affective Computing and Sentiment Analysis)
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17 pages, 5313 KiB  
Article
Understanding Learners’ Perception of MOOCs Based on Review Data Analysis Using Deep Learning and Sentiment Analysis
by Xieling Chen, Fu Lee Wang, Gary Cheng, Man-Kong Chow and Haoran Xie
Future Internet 2022, 14(8), 218; https://doi.org/10.3390/fi14080218 - 25 Jul 2022
Cited by 4 | Viewed by 1981
Abstract
Massive open online courses (MOOCs) have exploded in popularity; course reviews are important sources for exploring learners’ perceptions about different factors associated with course design and implementation. This study aims to investigate the possibility of automatic classification for the semantic content of MOOC [...] Read more.
Massive open online courses (MOOCs) have exploded in popularity; course reviews are important sources for exploring learners’ perceptions about different factors associated with course design and implementation. This study aims to investigate the possibility of automatic classification for the semantic content of MOOC course reviews to understand factors that can predict learners’ satisfaction and their perceptions of these factors. To do this, this study employs a quantitative research methodology based on sentiment analysis and deep learning. Learners’ review data from Class Central are analyzed to automatically identify the key factors related to course design and implementation and the learners’ perceptions of these factors. A total of 186,738 review sentences associated with 13 subject areas are analyzed, and consequently, seven course factors that learners frequently mentioned are found. These factors include: “Platforms and tools”, “Course quality”, “Learning resources”, “Instructor”, “Relationship”, “Process”, and “Assessment”. Subsequently, each factor is assigned a sentimental value using lexicon-driven methodologies, and the topics that can influence learners’ learning experiences the most are decided. In addition, learners’ perceptions across different topics and subjects are explored and discussed. The findings of this study contribute to helping MOOC instructors in tailoring course design and implementation to bring more satisfactory learning experiences for learners. Full article
(This article belongs to the Special Issue Affective Computing and Sentiment Analysis)
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13 pages, 937 KiB  
Article
Tell Me More: Automating Emojis Classification for Better Accessibility and Emotional Context Recognition
by Muhammad Atif and Valentina Franzoni
Future Internet 2022, 14(5), 142; https://doi.org/10.3390/fi14050142 - 05 May 2022
Cited by 8 | Viewed by 3039
Abstract
Users of web or chat social networks typically use emojis (e.g., smilies, memes, hearts) to convey in their textual interactions the emotions underlying the context of the communication, aiming for better interpretability, especially for short polysemous phrases. Semantic-based context recognition tools, employed in [...] Read more.
Users of web or chat social networks typically use emojis (e.g., smilies, memes, hearts) to convey in their textual interactions the emotions underlying the context of the communication, aiming for better interpretability, especially for short polysemous phrases. Semantic-based context recognition tools, employed in any chat or social network, can directly comprehend text-based emoticons (i.e., emojis created from a combination of symbols and characters) and translate them into audio information (e.g., text-to-speech readers for individuals with vision impairment). On the other hand, for a comprehensive understanding of the semantic context, image-based emojis require image-recognition algorithms. This study aims to explore and compare different classification methods for pictograms, applied to emojis collected from Internet sources. Each emoji is labeled according to the basic Ekman model of six emotional states. The first step involves extraction of emoji features through convolutional neural networks, which are then used to train conventional supervised machine learning classifiers for purposes of comparison. The second experimental step broadens the comparison to deep learning networks. The results reveal that both the conventional and deep learning classification approaches accomplish the goal effectively, with deep transfer learning exhibiting a highly satisfactory performance, as expected. Full article
(This article belongs to the Special Issue Affective Computing and Sentiment Analysis)
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28 pages, 6013 KiB  
Article
Measuring Ethical Values with AI for Better Teamwork
by Erkin Altuntas, Peter A. Gloor and Pascal Budner
Future Internet 2022, 14(5), 133; https://doi.org/10.3390/fi14050133 - 27 Apr 2022
Cited by 2 | Viewed by 3345
Abstract
Do employees with high ethical and moral values perform better? Comparing personality characteristics, moral values, and risk-taking behavior with individual and team performance has long been researched. Until now, these determinants of individual personality have been measured through surveys. However, individuals are notoriously [...] Read more.
Do employees with high ethical and moral values perform better? Comparing personality characteristics, moral values, and risk-taking behavior with individual and team performance has long been researched. Until now, these determinants of individual personality have been measured through surveys. However, individuals are notoriously bad at self-assessment. Combining machine learning (ML) with social network analysis (SNA) and natural language processing (NLP), this research draws on email conversations to predict the personal values of individuals. These values are then compared with the individual and team performance of employees. This prediction builds on a two-layered ML model. Building on features of social network structure, network dynamics, and network content derived from email conversations, we predict personality characteristics, moral values, and the risk-taking behavior of employees. In turn, we use these values to predict individual and team performance. Our results indicate that more conscientious and less extroverted team members increase the performance of their teams. Willingness to take social risks decreases the performance of innovation teams in a healthcare environment. Similarly, a focus on values such as power and self-enhancement increases the team performance of a global services provider. In sum, the contributions of this paper are twofold: it first introduces a novel approach to measuring personal values based on “honest signals” in emails. Second, these values are then used to build better teams by identifying ideal personality characteristics for a chosen task. Full article
(This article belongs to the Special Issue Affective Computing and Sentiment Analysis)
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