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Application of Artificial Intelligence Methods in Processing of Emotions, Decisions and Opinions

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 26811

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Special Issue Editors

Dr. Pawel Dybala

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Guest Editor

Institute of Middle and Far Eastern Studies, Faculty of International and Political Studies, Jagiellonian University, 30-387 Krakow, Poland
Interests: natural language processing; dialogue processing; humor processing; HCI; information retrieval
Special Issues, Collections and Topics in MDPI journals

Dr. Rafal Rzepka

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Guest Editor

Langauge Media Laboratory, Hokkaido University, Sapporo 060-0808, Japan
Interests: natural language processing; common sense knowledge retrieval; dialog processing; artificial general intelligence; affect and sentiment analysis; machine ethics
Special Issues, Collections and Topics in MDPI journals

Dr. Michal Ptaszynski

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Guest Editor

Text Information Processing Laboratory, Kitami Institute of Technology, 165 Koen-cho, Kitami 090-8507, Japan
Interests: natural language processing; artificial intelligence; affective computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

During recent years, social infrastructure has become irreversibly linked to the Internet through its everyday manifestations, such as social networking services (Twitter, Facebook, etc.). Every second this new tangible information-based reality provides large amounts of data filled with 1) emotional expressions; 2) people's opinions on various topics; and 3) their reasoning, revealing their decision-making processes. As these three categories are also closely interrelated with each other, they should be studied together to obtain a more robust view on all of the topics involved. This, as never before, provides an opportunity for the development and application of natural language processing methods, in particular those regarding such topics as emotion processing, decision making, and opinion mining.

For this issue, we invite high-quality papers from researchers with interest in knowing more about those topics and their connection to the world we live in by the means of opinion and sentiment analysis, recommendation systems, web mining, automated decision making, etc. We also invite papers on the topic of using Natural Language Processing tools and methods to process emotions, metaphors, ethics, or other phenomena related to human activities.

Dr. Pawel Dybala
Dr. Rafal Rzepka
Dr. Michal Ptaszynski
Guest Editors

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Keywords

emotions
decisions
opinions
NLP
AI
linguistics
cognitive science

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Published Papers (11 papers)

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Editorial

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3 pages, 141 KiB

Open AccessEditorial

Application of Artificial Intelligence Methods in Processing of Emotions, Decisions, and Opinions

by Michal Ptaszynski, Pawel Dybala and Rafal Rzepka

Appl. Sci. 2024, 14(13), 5912; https://doi.org/10.3390/app14135912 - 6 Jul 2024

Viewed by 673

Abstract

The rapid advancement of artificial intelligence (AI) and natural language processing (NLP) has profoundly impacted our understanding of emotions, decision-making, and opinions, particularly within the context of the Internet and social media [...] Full article

(This article belongs to the Special Issue Application of Artificial Intelligence Methods in Processing of Emotions, Decisions and Opinions)

Research

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18 pages, 11091 KiB

Open AccessArticle

A New AI Approach by Acquisition of Characteristics in Human Decision-Making Process

by Yuan Zhou and Siamak Khatibi

Appl. Sci. 2024, 14(13), 5469; https://doi.org/10.3390/app14135469 - 24 Jun 2024

Cited by 1 | Viewed by 1072

Abstract

Planning and decision making are closely interconnected processes that often occur in tandem, influence and informing each other. Planning usually precedes decision making in the chronological sequence, and it can be viewed as a strategy to make decisions. A comprehensive planning or decision strategy can facilitate effective decisions. Thus, understanding and learning human decision-making strategies has drawn intensive attention from the AI community. For example, applying planning algorithms into reinforcement leaning (RL) can simulate the consequence of different actions and select optimal decisions based on learned models, while inverse reinforcement learning (IRL) learns a reward function and policy from expert demonstration and applies them into new scenarios. Most of these methods work based on learning human decision strategies by using modeling of a Markovian decision-making process (MDP). In this paper, we argue that the property of MDP is not fit for human decision-making processes in the real-world and it is insufficient to capture human decision strategies. To tackle this challenge, we propose a new approach to identify the characteristics of human decision-making processes as a decision map, where the decision strategy is defined by the probability distribution of human decisions that are adaptive to the dynamic changes in the environment. The proposed approach was inspired by imitation learning (IL) but with fundamental differences: (a) Instead of aiming to learn an optimal policy based on expert’s demonstrations, we aimed to estimate the distribution of decisions of any group of people. (b) Instead of modeling the environment by an MDP, we used an ambiguity probability model to consider the uncertainty of each decision. (c) The participant trajectory was obtained by categorizing each decision of a participant to a certain cluster based on the commonness in the distribution of decisions. The result shows a feasible way to capture human long-term decision dependency, which provides a complement to the existing machine learning methods for understanding and learning human decision strategies. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence Methods in Processing of Emotions, Decisions and Opinions)

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20 pages, 4718 KiB

Open AccessArticle

Combining Balancing Dataset and SentenceTransformers to Improve Short Answer Grading Performance

by Maresha Caroline Wijanto and Hwan-Seung Yong

Appl. Sci. 2024, 14(11), 4532; https://doi.org/10.3390/app14114532 - 25 May 2024

Cited by 1 | Viewed by 851

Abstract

Short-answer questions can encourage students to express their understanding. However, these answers can vary widely, leading to subjective assessments. Automatic short answer grading (ASAG) has become an important field of research. Recent studies have demonstrated a good performance using computationally expensive models. Additionally, available datasets are often unbalanced in terms of quantity. This research attempts to combine a simpler SentenceTransformers model with a balanced dataset, using prompt engineering in GPT to generate new sentences. Our recommended model also tries to fine-tune several hyperparameters to achieve optimal results. The research results show that the relatively small-sized all-distilroberta-v1 model can achieve a Pearson correlation value of 0.9586. The RMSE, F1-score, and accuracy score also provide better performances. This model is combined with the fine-tuning of hyperparameters, such as the use of gradient checkpointing, the split-size ratio for testing and training data, and the pre-processing steps. The best result is obtained when the new generated dataset from the GPT data augmentation is implemented. The newly generated dataset from GPT data augmentation achieves a cosine similarity score of 0.8 for the correct category. When applied to other datasets, our proposed method also shows an improved performance. Therefore, we conclude that a relatively small-sized model combined with the fine-tuning of the appropriate hyperparameters and a balanced dataset can provide performance results that surpass other models that require larger resources and are computationally expensive. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence Methods in Processing of Emotions, Decisions and Opinions)

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25 pages, 4322 KiB

Open AccessArticle

Text Mining and Multi-Attribute Decision-Making-Based Course Improvement in Massive Open Online Courses

by Pei Yang, Ying Liu, Yuyan Luo, Zhong Wang and Xiaoli Cai

Appl. Sci. 2024, 14(9), 3654; https://doi.org/10.3390/app14093654 - 25 Apr 2024

Cited by 1 | Viewed by 1195

Abstract

As the leading platform of online education, MOOCs provide learners with rich course resources, but course designers are still faced with the challenge of how to accurately improve the quality of courses. Current research mainly focuses on learners’ emotional feedback on different course attributes, neglecting non-emotional content as well as the costs required to improve these attributes. This limitation makes it difficult for course designers to fully grasp the real needs of learners and to accurately locate the key issues in the course. To overcome the above challenges, this study proposes an MOOC improvement method based on text mining and multi-attribute decision-making. Firstly, we utilize word vectors and clustering techniques to extract course attributes that learners focus on from their comments. Secondly, with the help of some deep learning methods based on BERT, we conduct a sentiment analysis on these comments to reveal learners’ emotional tendencies and non-emotional content towards course attributes. Finally, we adopt the multi-attribute decision-making method TOPSIS to comprehensively consider the emotional score, attention, non-emotional content, and improvement costs of the attributes, providing course designers with a priority ranking for attribute improvement. We applied this method to two typical MOOC programming courses—C language and Java language. The experimental findings demonstrate that our approach effectively identifies course attributes from reviews, assesses learners’ satisfaction, attention, and cost of improvement, and ultimately generates a prioritized list of course attributes for improvement. This study provides a new approach for improving the quality of online courses and contributes to the sustainable development of online course quality. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence Methods in Processing of Emotions, Decisions and Opinions)

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32 pages, 1277 KiB

Open AccessArticle

Automatic Vulgar Word Extraction Method with Application to Vulgar Remark Detection in Chittagonian Dialect of Bangla

by Tanjim Mahmud, Michal Ptaszynski and Fumito Masui

Appl. Sci. 2023, 13(21), 11875; https://doi.org/10.3390/app132111875 - 30 Oct 2023

Cited by 30 | Viewed by 1844

Abstract

The proliferation of the internet, especially on social media platforms, has amplified the prevalence of cyberbullying and harassment. Addressing this issue involves harnessing natural language processing (NLP) and machine learning (ML) techniques for the automatic detection of harmful content. However, these methods encounter challenges when applied to low-resource languages like the Chittagonian dialect of Bangla. This study compares two approaches for identifying offensive language containing vulgar remarks in Chittagonian. The first relies on basic keyword matching, while the second employs machine learning and deep learning techniques. The keyword-matching approach involves scanning the text for vulgar words using a predefined lexicon. Despite its simplicity, this method establishes a strong foundation for more sophisticated ML and deep learning approaches. An issue with this approach is the need for constant updates to the lexicon. To address this, we propose an automatic method for extracting vulgar words from linguistic data, achieving near-human performance and ensuring adaptability to evolving vulgar language. Insights from the keyword-matching method inform the optimization of machine learning and deep learning-based techniques. These methods initially train models to identify vulgar context using patterns and linguistic features from labeled datasets. Our dataset, comprising social media posts, comments, and forum discussions from Facebook, is thoroughly detailed for future reference in similar studies. The results indicate that while keyword matching provides reasonable results, it struggles to capture nuanced variations and phrases in specific vulgar contexts, rendering it less robust for practical use. This contradicts the assumption that vulgarity solely relies on specific vulgar words. In contrast, methods based on deep learning and machine learning excel in identifying deeper linguistic patterns. Comparing SimpleRNN models using Word2Vec and fastText embeddings, which achieved accuracies ranging from 0.84 to 0.90, logistic regression (LR) demonstrated remarkable accuracy at 0.91. This highlights a common issue with neural network-based algorithms, namely, that they typically require larger datasets for adequate generalization and competitive performance compared to conventional approaches like LR. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence Methods in Processing of Emotions, Decisions and Opinions)

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18 pages, 3359 KiB

Open AccessArticle

Social Media Opinion Analysis Model Based on Fusion of Text and Structural Features

by Jie Long, Zihan Li, Qi Xuan, Chenbo Fu, Songtao Peng and Yong Min

Appl. Sci. 2023, 13(12), 7221; https://doi.org/10.3390/app13127221 - 16 Jun 2023

Cited by 1 | Viewed by 1649

Abstract

The opinion recognition for comments in Internet media is a new task in text analysis. It takes comment statements as the research object, by learning the opinion tendency in the original text with annotation, and then performing opinion tendency recognition on the unannotated statements. However, due to the uncertainty of NLP (natural language processing) in short scenes and the complexity of Chinese text, existing methods have some limitations in accuracy and application scenarios. In this paper, we propose an opinion tendency recognition model HGAT (heterogeneous graph attention network) that integrates text vector and context structure methods to address the above problems. This method first trains a text vectorization model based on annotation text content, then constructs an isomorphic graph with annotation, news, and theme as its apex, and then optimizes the feature vectors of all nodes using an isomorphic graph neural network model with attention mechanism. In addition, this article collected 1,684,318 news items and 57,845,091 comments based on Toutiao, sifted through 511 of those stories and their corresponding 103,787 comments, and tested the impact of HGAT on this dataset. Experiments show that this method has stable improvement effect on different NLP methods, increasing accuracy by 2–10%, and provides a new perspective for opinion tendency recognition. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence Methods in Processing of Emotions, Decisions and Opinions)

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15 pages, 8580 KiB

Open AccessArticle

Emotional State Detection Using Electroencephalogram Signals: A Genetic Algorithm Approach

by Rosa A. García-Hernández, José M. Celaya-Padilla, Huizilopoztli Luna-García, Alejandra García-Hernández, Carlos E. Galván-Tejada, Jorge I. Galván-Tejada, Hamurabi Gamboa-Rosales, David Rondon and Klinge O. Villalba-Condori

Appl. Sci. 2023, 13(11), 6394; https://doi.org/10.3390/app13116394 - 23 May 2023

Cited by 7 | Viewed by 2319

Abstract

Emotion recognition based on electroencephalogram signals (EEG) has been analyzed extensively in different applications, most of them using medical-grade equipment in laboratories. The trend in human-centered artificial intelligence applications is toward using portable sensors with reduced size and improved portability that can be taken to real life scenarios, which requires systems that efficiently analyze information in real time. Currently, there is no specific set of features or specific number of electrodes defined to classify specific emotions using EEG signals, and performance may be improved with the combination of all available features but could result in high dimensionality and even worse performance; to solve the problem of high dimensionality, this paper proposes the use of genetic algorithms (GA) to automatically search the optimal subset of EEG data for emotion classification. Publicly available EEG data with 2548 features describing the waves related to different emotional states are analyzed, and then reduced to 49 features with genetic algorithms. The results show that only 49 features out of the 2548 can be sufficient to create machine learning (ML) classification models with, using algorithms such as k-nearest neighbor (KNN), random forests (RF) and artificial neural networks (ANN), obtaining results with 90.06%, 93.62% and 95.87% accuracy, respectively, which are higher than the 87.16% and 89.38% accuracy of previous works. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence Methods in Processing of Emotions, Decisions and Opinions)

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26 pages, 1394 KiB

Open AccessArticle

Affect Analysis in Arabic Text: Further Pre-Training Language Models for Sentiment and Emotion

by Wafa Alshehri, Nora Al-Twairesh and Abdulrahman Alothaim

Appl. Sci. 2023, 13(9), 5609; https://doi.org/10.3390/app13095609 - 1 May 2023

Cited by 5 | Viewed by 2835

Abstract

One of the main tasks in the field of natural language processing (NLP) is the analysis of affective states (sentiment and emotional) based on written text, and attempts have improved dramatically in recent years. However, in studies on the Arabic language, machine learning or deep learning algorithms were utilised to analyse sentiment and emotion more often than current pre-trained language models. Additionally, further pre-training the language model on specific tasks (i.e., within-task and cross-task adaptation) has not yet been investigated for Arabic in general, and for the sentiment and emotion task in particular. In this paper, we adapt a BERT-based Arabic pretrained language model for the sentiment and emotion tasks by further pre-training it on a sentiment and emotion corpus. Hence, we developed five new Arabic models: QST, QSR, QSRT, QE3, and QE6. Five sentiment and two emotion datasets spanning both small- and large-resource settings were used to evaluate the developed models. The adaptation approaches significantly enhanced the performance of seven Arabic sentiment and emotion datasets. The developed models showed excellent improvements over the sentiment and emotion datasets, which ranged from 0.15–4.71%. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence Methods in Processing of Emotions, Decisions and Opinions)

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35 pages, 1205 KiB

Open AccessFeature PaperArticle

Personality Types and Traits—Examining and Leveraging the Relationship between Different Personality Models for Mutual Prediction

by Dušan Radisavljević, Rafal Rzepka and Kenji Araki

Appl. Sci. 2023, 13(7), 4506; https://doi.org/10.3390/app13074506 - 2 Apr 2023

Cited by 7 | Viewed by 5950

Abstract

The popularity of social media services has led to an increase of personality-relevant data in online spaces. While the majority of people who use these services tend to express their personality through measures offered by the Myers–Briggs Type Indicator (MBTI), another personality model known as the Big Five has been a dominant paradigm in academic works that deal with personality research. In this paper, we seek to bridge the gap between the MBTI, Big Five and another personality model known as the Enneagram of Personality, with the goal of increasing the amount of resources for the Big Five model. We further explore the relationship that was previously reported between the MBTI types and certain Big Five traits as well as test for the presence of a similar relationship between Enneagram and Big Five measures. We propose a new method relying on psycholingusitc features selected based on their relationship with the MBTI model. This approach showed the best performance through our experiments and led to an increase of up to 3% in automatic personality recognition for Big Five traits on the per-trait level. Our detailed experimentation offers further insight into the nature of personality and into how well it translates between different personality models. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence Methods in Processing of Emotions, Decisions and Opinions)

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16 pages, 2325 KiB

Open AccessArticle

Understanding of Customer Decision-Making Behaviors Depending on Online Reviews

by Yeo-Gyeong Noh, Junryeol Jeon and Jin-Hyuk Hong

Appl. Sci. 2023, 13(6), 3949; https://doi.org/10.3390/app13063949 - 20 Mar 2023

Cited by 4 | Viewed by 3306

Abstract

With a never-ending stream of reviews propagating online, consumers encounter countless good and bad reviews. Depending on which reviews consumers read, they get a different impression of the product. In this paper, we focused on the relationship between the text and numerical information of reviews to gain a better understanding of the decision-making process of consumers affected by the reviews. We evaluated the decisions that consumers made when encountering the review structure of star ratings paired with comments, with respect to three research questions: (1) how consumers compare two products with reviews, (2) how they individually perceive a product based on the corresponding reviews, and (3) how they interpret star ratings and comments. Through the user study, we confirmed that consumers consider reviews differently according to product presentation conditions. When consumers were comparing products, they were more influenced by star ratings, whereas when they were evaluating individual products, they were more influenced by comments. Additionally, consumers planning to buy a product examined star ratings by more stringent criteria than those who had already purchased the product. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence Methods in Processing of Emotions, Decisions and Opinions)

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Review

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19 pages, 2231 KiB

Open AccessReview

Fake News Detection on Social Networks: A Survey

by Yanping Shen, Qingjie Liu, Na Guo, Jing Yuan and Yanqing Yang

Appl. Sci. 2023, 13(21), 11877; https://doi.org/10.3390/app132111877 - 30 Oct 2023

Cited by 2 | Viewed by 3342

Abstract

In recent years, social networks have developed rapidly and have become the main platform for the release and dissemination of fake news. The research on fake news detection has attracted extensive attention in the field of computer science. Fake news detection technology has made many breakthroughs recently, but many challenges remain. Although there are some review papers on fake news detection, a more detailed picture for carrying out a comprehensive review is presented in this paper. The concepts related to fake news detection, including fundamental theory, feature type, detection technique and detection approach, are introduced. Specifically, through extensive investigation and complex organization, a classification method for fake news detection is proposed. The datasets of fake news detection in different fields are also compared and analyzed. In addition, the tables and pictures summarized here help researchers easily grasp the full picture of fake news detection. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence Methods in Processing of Emotions, Decisions and Opinions)

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