Review
Computer Science, Information Systems
Miguel A. Alonso, David Vilares, Carlos Gomez-Rodriguez, Jesus Vilares
Summary: Fake news has been on the rise in recent years, posing a serious threat to social cohesion and trust in leaders. Automatic systems for fake news detection have become increasingly important due to the unfeasibility of manual verification, with sentiment analysis playing a key role in this process.
Article
Computer Science, Artificial Intelligence
Keith Cortis, Brian Davis
Summary: Social Opinion Mining research focuses on identifying various opinion dimensions in user-generated content across social media platforms, contributing to the evolution of Artificial Intelligence and real-world applications in marketing, politics, and healthcare. The in-depth analysis covers aspects such as social media platforms, techniques, social datasets, tools and technologies, and future research directions.
ARTIFICIAL INTELLIGENCE REVIEW
(2021)
Article
Psychology, Multidisciplinary
Valeria A. Pfeifer, Penny M. Pexman
Summary: Verbal irony is pervasive in social interaction and can achieve a variety of communicative goals and effects. Contrary to its negative reputation, this article presents evidence for the cognitive, social, and emotional benefits of verbal irony and highlights its potential to provide crucial psychological insights. The power of irony lies in its ability to create meaning that conflicts with the literal meaning, thereby enhancing cognition, mediating emotions, and shaping social relationships.
CURRENT DIRECTIONS IN PSYCHOLOGICAL SCIENCE
(2023)
Article
Computer Science, Artificial Intelligence
Ibtissam Touahri, Azzeddine Mazroui
Summary: This article discusses how to improve the accuracy of sentiment analysis system by utilizing sarcastic features, which is challenging due to the implicit nature of sarcasm and incongruity in context. By extracting features, building sentimental, offensive, and sarcastic lexicons, as well as collecting corpora, enhancements were made to the sentiment analysis system.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Environmental Sciences
Ji Min, Han Yunxiu, Sun Yong, Jin Fengxiang, Li Ting
Summary: Research on sentiment analysis of public opinions related to air quality on social media, education is found to be the most significant influence factor on judging the level of sentiment, proposing a method to calculate opinions sentiment value.
WATER AIR AND SOIL POLLUTION
(2021)
Article
Immunology
Hilary Piedrahita-Valdes, Diego Piedrahita-Castillo, Javier Bermejo-Higuera, Patricia Guillem-Saiz, Juan Ramon Bermejo-Higuera, Javier Guillem-Saiz, Juan Antonio Sicilia-Montalvo, Francisco Machio-Regidor
Summary: Vaccine hesitancy was identified as a major global health threat in 2019 by the World Health Organisation. Social media plays a significant role in the dissemination of information related to vaccines. Monitoring vaccine-related conversations on platforms like Twitter can help identify factors contributing to vaccine confidence over time and in different geographical locations.
Article
Communication
Sim-Mei Choo, Estella Chee Li Lim, Chu-Ting Chang, Yin-Chi Li, Yung-Chun Chang, Shabbir Syed-Abdul
Summary: As a contested state, Taiwan emphasizes its embattled democracy through the #TaiwanCanHelp hashtag activism to gain international support and awareness against the authoritarian government in Beijing. This study quantifies the public response and uncovers public opinions on Twitter about #TaiwanCanHelp, showing that it has resonated globally and successfully generated interest through digitalization of diplomacy.
SOCIAL MEDIA + SOCIETY
(2022)
Article
Computer Science, Artificial Intelligence
Michael Wiegand, Marc Schulder, Josef Ruppenhofer
Summary: This paper examines the binary classification of sentiment views for verbal multiword expressions (MWEs), distinguishing between MWEs conveying the view of the speaker and MWEs conveying the view of explicit entities. Novel features considering the internal structure of MWEs, a unigram sentiment-view lexicon, and information from Wiktionary are proposed. The study also shows the impact of the corpus used for representation induction on classification, and demonstrates the improvement of a state-of-the-art classifier trained on BERT using the learnt knowledge. Feature-based approach outperforms generic methods for MWEs, similar to unigrams.
NATURAL LANGUAGE ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Abdalsamad Keramatfar, Hossein Amirkhani, Amir Jalaly Bidgoly
Summary: This paper utilizes graph structures for sentiment analysis of microblog posts and proposes a context-aware sentiment analysis approach using a stacking model that employs multiple graph types. Experimental results demonstrate that this method outperforms baselines and state-of-the-art models on a real-world Twitter sentiment analysis dataset.
COGNITIVE COMPUTATION
(2022)
Review
Computer Science, Artificial Intelligence
Chinmayee Sahoo, Mayur Wankhade, Binod Kumar Singh
Summary: This research article provides a comprehensive review of using deep learning techniques in sentiment analysis. It covers various aspects of sentiment analysis, including data preprocessing, feature extraction, model architectures, and evaluation metrics. The study explores the application of recurrent neural networks, convolutional neural networks, and transformer models in sentiment analysis, as well as the utilization of long short-term memory and gated recurrent unit to model sequential dependencies in text data. The findings from this review can aid in the development of more accurate and efficient sentiment analysis models, benefiting organizations in gaining insights from large volumes of textual data in various domains.
INTERNATIONAL JOURNAL OF MULTIMEDIA INFORMATION RETRIEVAL
(2023)
Article
Computer Science, Information Systems
Belgacem Brahimi, Mohamed Touahria, Abdelkamel Tari
Summary: This paper proposes methods to extract valuable opinions from online movie reviews using n-gram and skip-n-gram models, subjective words, and feature reduction techniques to enhance sentiment analysis in Arabic. Experimental results demonstrate the effectiveness of these methods in improving sentiment classification results.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2021)
Article
Computer Science, Information Systems
Ibrahim Abu Farha, Walid Magdy
Summary: This study conducted a comprehensive comparative analysis of the most effective methods used for Arabic sentiment analysis, demonstrating the superior performance of transformer-based language models. The research also highlighted the limitations of existing annotated Arabic SA datasets and the challenge of sarcasm prevalent in Arabic dialects.
INFORMATION PROCESSING & MANAGEMENT
(2021)
Review
Computer Science, Interdisciplinary Applications
Tasnim M. A. Zayet, Maizatul Akmar Ismail, Kasturi Dewi Varathan, Rafidah M. D. Noor, Hui Na Chua, Angela Lee, Yeh Ching Low, Sheena Kaur Jaswant Singh
Summary: This paper summarizes the research on social media analysis in the field of transportation over the past decade, revealing research trends by countries, platform usage trends, and the most commonly used analytical methods and data. Finally, challenges and directions for future research are proposed.
Article
Computer Science, Information Systems
C. Y. Ng, Kris M. Y. Law, Andrew W. H. Ip
Summary: In the world of social networking, consumers tend to rely on expert comments or product reviews before making buying decisions. However, online posts are often short and may contain both positive and negative sentiments, making it challenging to accurately determine sentiment polarity.
JOURNAL OF ORGANIZATIONAL AND END USER COMPUTING
(2021)
Article
Business, Finance
Jin Shao, Jingke Hong, Xianzhu Wang, Xiaochen Yan
Summary: This study investigates the dynamic relationship between house prices and social media sentiment in China. A housing sentiment index is created using natural language processing techniques, and wavelet analysis is used to examine causal correlations. The findings show that the sentiment index is negatively correlated with house price fluctuations overall. The long-term relationship between house prices and sentiment is bidirectional, while house prices causally affect sentiment in the short term. Additionally, sentiment significantly impacts house prices in third-tier cities and the western regions.
FINANCE RESEARCH LETTERS
(2023)
Article
Computer Science, Artificial Intelligence
Robbert De Troij, Stefan Grondelaers, Dirk Speelman, Antal van den Bosch
Summary: This article uses computational tools to investigate the syntactic relationship between Belgian Dutch and Netherlandic Dutch, finding that lexical input works well for Netherlandic Dutch but not as effectively for Belgian Dutch.
NATURAL LANGUAGE ENGINEERING
(2022)
Article
Business
Christine Liebrecht, Christina Tsaousi, Charlotte van Hooijdonk
Summary: This study delves into the operationalization of CHV in online brand communication, presenting a taxonomy of linguistic elements related to message personalization, informal speech, and invitational rhetoric. It also discusses how these operationalizations contribute to consumers' perceptions of CHV and their evaluation regarding the message and the brand, while providing directions for future research and managerial implications.
JOURNAL OF BUSINESS RESEARCH
(2021)
Article
Linguistics
Antoinette Luijkx, Marinel Gerritsen, Margot van Mulken
Summary: The study examined the challenges Dutch learners face when using German in a professional context and how to maintain good relationships with native German speakers. The results showed that pragmalinguistic issues are more significant in German writing courses.
LANGUAGE AWARENESS
(2022)
Article
Linguistics
Dirk Pijpops, Dirk Speelman, Antal van den Bosch
Summary: According to usage-based linguistics, language variation addresses the functional need of language users, which depends on the lexical realization of different constructions. This paper develops a data-driven approach to study language variation and applies it to investigate the Dutch naar-alternation.
LINGUISTICS VANGUARD
(2022)
Article
Communication
Sandra Jacobs, Christine Liebrecht
Summary: This study examines the impact of tone, response strategy, and user involvement in webcare on participants' continuance intention and perceptions of reputation in a public sector context. The results indicate that using a conversational human voice in webcare contributes to reputation management and increases continuance intention, while response strategy and user involvement have minimal impacts.
JOURNAL OF COMMUNICATION MANAGEMENT
(2023)
Article
Health Care Sciences & Services
Lea Loesch, Teun Zuiderent-Jerak, Florian Kunneman, Elena Syurina, Marloes Bongers, Mart L. Stein, Michelle Chan, Willemine Willems, Aura Timen
Summary: This study explores the potential of artificial intelligence (AI)-based methods to capture experience-based knowledge and value considerations from existing data channels for guideline development. The findings show that natural language processing (NLP) methods can identify and analyze experience-based knowledge and provide valuable insights for guideline development. This knowledge can help identify problems with guideline application and contribute to the revision of guideline text.
JOURNAL OF MEDICAL INTERNET RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Fabian Ferrari, Jose van Dijck, Antal van den Bosch
Summary: In order to ensure the integrity of knowledge production, it is necessary to provide regulators and researchers with access to the training procedures of foundational models like GPT-4. Foundation models need to be open and accessible, although they are not synonymous.
NATURE MACHINE INTELLIGENCE
(2023)
Article
Communication
Luuk Lagerwerf, Margot Van Mulken, Jefta B. Lagerwerf
Summary: The levels of conceptual similarity in equivalent visual structures can determine how meaning is attributed to images. Visual hyponyms are interpreted more quickly and appreciated more than visual metaphors and unrelated objects.
FRONTIERS IN COMMUNICATION
(2023)
Article
Communication
Fabian Ferrari, Jose van Dijck, Antal van den Bosch
Summary: The absence of benchmarks to examine the effectiveness of oversight mechanisms for generative AI systems is a problem for research and policy. This article introduces the conditions of industrial observability, public inspectability, and technical modifiability as structural elements for governing generative AI systems. These conditions are exemplified using the EU's AI Act, grounding the analysis of oversight mechanisms in the material properties of generative AI systems.
NEW MEDIA & SOCIETY
(2023)
Article
Linguistics
Julia Udden, Annika Hulten, Jan-Mathijs Schoffelen, Nietzsche Lam, Karin Harbusch, Antal van den Bosch, Gerard Kempen, Karl Magnus Petersson, Peter Hagoort
Summary: This study investigates the independence of sentence processing beyond single words and the network parts sensitive to syntactic complexity. The findings show that a left-hemisphere frontotemporoparietal network is supramodal and the left inferior frontal gyrus and the left posterior middle temporal gyrus are associated with different complexities. These findings have implications for neurobiological models of language processing.
NEUROBIOLOGY OF LANGUAGE
(2022)
Article
Computer Science, Artificial Intelligence
Jinbiao Yang, Antal van den Bosch, Stefan L. Frank
Summary: This study investigates the cognitive units during reading and finds that model-segmented units predict eye fixations better than word units. The results support the theory that the mental lexicon stores not only words but also smaller and larger units.
FRONTIERS IN ARTIFICIAL INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Naomi Kamoen, Christine Liebrecht
Summary: Research shows that VAA with a conversational agent function improves users' political knowledge and accuracy of answering questions compared to traditional VAA. The structured design of CAVAA provides a better user experience in terms of accessing additional information and evaluation.
FRONTIERS IN ARTIFICIAL INTELLIGENCE
(2022)
Article
Audiology & Speech-Language Pathology
M. Bentum, L. Ten Bosch, A. van den Bosch, M. Ernestus
Summary: This study investigated the influence of speech register on predictive language processing using the N400 effect. The results show that the amplitude of the N400 is best predicted by register-specific word surprisal, indicating that the statistics of the wider context (i.e., register) influence predictive language processing. Furthermore, adaptation to speech register is not solely explained by recency effects; instead, listeners adjust their word anticipations based on the presented speech register.
BRAIN AND LANGUAGE
(2022)
Article
Communication
Charlotte van Hooijdonk, Christine Liebrecht
Summary: The study revealed that offering an apology is the most frequently used response strategy in webcare conversations, with accommodative strategies being more common than defensive ones. While the presence of an apology alone does not enhance brand reputation, a combination of defensive and accommodative strategies proves to be effective in protecting reputation.
DISCOURSE CONTEXT & MEDIA
(2021)
Article
Linguistics
Chara Tsoukala, Stefan L. Frank, Antal Van den Bosch, Jorge Valdes Kroff, Mirjam Broersma
Summary: Spanish-English bilinguals show an asymmetry in code-switching between the auxiliary verbs haber and estar, possibly due to the semantic weight of estar as a main verb. This was tested using a connectionist model that demonstrated the disappearance of the asymmetry when haber was used as a main verb, supporting the hypothesis of lack of semantic weight causing the asymmetry.
BILINGUALISM-LANGUAGE AND COGNITION
(2021)
Article
Computer Science, Information Systems
Sang-Bing Tsai, Xusen Cheng, Yanwu Yang, Jason Xiong, Alex Zarifis
Summary: This article structurally concludes the methods proposed and evidenced to develop digital entrepreneurship from a socio-technical perspective. The technology itself and the process of utilization should be carefully considered. From a social perspective, fulfilling the needs of customers in social interaction and nurturing characteristics and social skills for the digital work environment are crucial.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Xiaochang Fang, Hongchen Wu, Jing Jing, Yihong Meng, Bing Yu, Hongzhu Yu, Huaxiang Zhang
Summary: This study proposes a novel fake news detection framework, utilizing news semantic environment perception (NSEP) to identify fake news content. The framework consists of steps such as dividing the semantic environment into macro and micro levels, applying graph convolutional networks, and utilizing multihead attention. Empirical experiments show that the NSEP framework achieves high accuracy in detecting Chinese fake news, outperforming other baseline methods and highlighting the importance of both micro and macro semantic environments in early detection of fake news.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Xudong Sun, Alladoumbaye Ngueilbaye, Kaijing Luo, Yongda Cai, Dingming Wu, Joshua Zhexue Huang
Summary: This paper proposes a scalable distributed frequent itemset mining (ScaDistFIM) algorithm to address the data scalability and flexibility issues in basket analysis in the big data era. Experiment results demonstrate that the ScaDistFIM algorithm is more efficient compared to the Spark FP-Growth algorithm.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Boxu Guan, Xinhua Zhu, Shangbo Yuan
Summary: This paper aims to improve the interpretability of machine reading comprehension models by utilizing the pre-trained T5 model for evidence inference. They propose an interpretable reading comprehension model based on T5, which is trained on a more accurate evidence corpus and can infer precise interpretations for answers. Experimental results show that their model outperforms the baseline BERT model on the SQuAD1.1 task.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Yanhao Wang, Baohua Zhang, Weikang Liu, Jiahao Cai, Huaping Zhang
Summary: In this study, we propose a data augmentation-based semantic text matching model called STMAP. By using Gaussian noise and noise mask signal for data augmentation, as well as employing an adaptive optimization network for training target optimization, our model achieves good performance in few-shot learning and semantic deviation problems.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Jiahao Yang, Shuo Feng, Wenkai Zhang, Ming Zhang, Jun Zhou, Pengyuan Zhang
Summary: To pursue profit from stock markets, researchers utilize deep learning methods to forecast asset price movements. However, there are two issues in current research, the discrepancy between forecasting results and profits, and heavy reliance on prior knowledge. To address these issues, researchers propose a novel optimization objective and modeling method, and conduct experiments to validate their approach.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Heng Zhang, Chengzhi Zhang, Yuzhuo Wang
Summary: This study provides an accurate analysis of technology development in the field of Natural Language Processing (NLP) from an entity-centric perspective. The findings indicate an increase in the average number of entities per paper, with pre-trained language models becoming mainstream and the impact of Wikipedia dataset and BLEU metric continuing to rise. There has been a surge in popularity for new high-impact technologies in recent years, with researchers accepting them at an unprecedented speed.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Davide Buscaldi, Danilo Dessi, Enrico Motta, Marco Murgia, Francesco Osborne, Diego Reforgiato Recupero
Summary: In scientific papers, citing other articles is a common practice to support claims and provide evidence. This paper proposes two automatic methods using Transformer models to address citation placement, and achieves significant improvements in experiments.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Baozhuang Niu, Lingfeng Wang, Xinhu Yu, Beibei Feng
Summary: This paper examines whether the incumbent brand should adopt digital technology to forecast demand and adjust order decisions in the face of soaring demand for medical supply caused by frequent outbreaks of regional COVID-19 epidemic. The study finds that digital transformation can lead to a triple-win situation among the incumbent brand, social welfare, and consumer surplus, as well as bring benefits to the manufacturer. Furthermore, the research provides insights for firms' digital entrepreneurship decisions through theoretical optimization and data processing/policy simulation.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Xueyang Qin, Lishang Li, Fei Hao, Meiling Ge, Guangyao Pang
Summary: Image-text retrieval is important in connecting vision and language. This paper proposes a method that utilizes prior knowledge to enhance feature representations and optimize network training for better retrieval results.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Review
Computer Science, Information Systems
Gang Ren, Lei Diao, Fanjia Guo, Taeho Hong
Summary: This paper proposes a novel approach for predicting the helpfulness of reviews by utilizing both textual and image features. The proposed method considers the correlation between features through self-attention and co-attention mechanisms, and fuses multi-modal features for prediction. Experimental results demonstrate the superior performance of the proposed method compared to benchmark methods.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Zhongquan Jian, Jiajian Li, Qingqiang Wu, Junfeng Yao
Summary: Aspect-Level Sentiment Classification (ALSC) is a crucial challenge in Natural Language Processing (NLP). Most existing methods fail to consider the correlations between different instances, leading to a lack of global viewpoint. To address this issue, we propose a Retrieval Contrastive Learning (RCL) framework that extracts intrinsic knowledge across instances for improved instance representation. Experimental results demonstrate that training ALSC models with RCL leads to substantial performance improvements.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Ying Hu, Yanping Chen, Ruizhang Huang, Yongbin Qin, Qinghua Zheng
Summary: Biomedical relation extraction aims to extract the interactive relations between biomedical entities in a sentence. This study proposes a hierarchical convolutional model to address the semantic overlapping and data imbalance problems. The model encodes both local contextual features and global semantic dependencies, enhancing the discriminability of the neural network for biomedical relation extraction.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Zhou Yang, Yucai Pang, Xuehong Li, Qian Li, Shihong Wei, Rong Wang, Yunpeng Xiao
Summary: This study proposes a rumor detection model based on topic audiolization, which transforms the topic space into audio-like signals. Experimental results show that the model achieves significant performance improvements in rumor identification.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Alistair Moffat
Summary: This paper proposes the buying power metric for assessing the quality of product rankings on e-commerce sites. It discusses the relationship between the buying power metric and user reactions, and introduces an alternative product ranking effectiveness metric.
INFORMATION PROCESSING & MANAGEMENT
(2024)