Review
Computer Science, Artificial Intelligence
David M. Goldberg, Alan S. Abrahams
Summary: Many firms struggle with monitoring product safety due to the potential negative impacts on consumers and financial standings. Monitoring online reviews can provide important safety insights, but the large volume of data poses practical challenges. This study proposes two new methods for identifying safety hazards, which show improvement over traditional approaches and demonstrate promise for cross-category analysis.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Review
Computer Science, Artificial Intelligence
David M. Goldberg, Alan S. Abrahams
Summary: In recent years, online reviews have become an important way for consumers to express their opinions and feedback. However, the unstructured and voluminous nature of textual data makes it challenging for companies to effectively utilize this feedback. This study proposes a method for prioritizing online reviews by using text mining tools, focusing on identifying the most useful reviews pertaining to innovation opportunities for firms. The results demonstrate the effectiveness of the proposed technique in improving upon existing methods, and senior managers at a large manufacturing firm also validate the usefulness of the selected attribute types in online reviews.
DECISION SUPPORT SYSTEMS
(2022)
Article
Computer Science, Information Systems
P. Anitha, Malini M. Patil
Summary: The objective of this study is to apply business intelligence in identifying potential customers in the Retail Industry by providing relevant and timely data. The curated and organized data based on systematic study and scientific applications in analyzing sales history and purchasing behavior not only enhances business sales and profit, but also predicts consumer purchasing behavior and related patterns.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Green & Sustainable Science & Technology
Shivam Singh, Manish Kumar Goyal
Summary: The increase in extreme weather events due to climate change poses significant threats to various sectors globally. Future climate projections suggest even more frequent and intense extreme events, which will further increase risks to socioeconomic infrastructure. Efforts are being made to incorporate climate risk assessment in financial decision-making, and strategies have been proposed to assess climate risk in various sectors and enhance business resilience to climate change. Artificial Intelligence and deep learning algorithms show promise in predicting weather and climate extremes and managing associated risks. A case study on predicting Atmospheric Rivers using deep learning algorithms supports their application in decision support systems for enhancing the climate resilience of businesses.
JOURNAL OF CLEANER PRODUCTION
(2023)
Article
Hospitality, Leisure, Sport & Tourism
Soo Yeon Kwak, Minjung Shin, Minwoo Lee, Ki-Joon Back
Summary: This study aims to integrate reviewers' and readers' perspectives on extremely negative reviews and examines the relationship between negative emotion intensity levels and reviews helpfulness on integrated websites and social networking sites (SNS). The findings reveal that integrated website reviewers express more extreme negative emotions than SNS reviewers, and both SNS and integrated website readers perceive reviews with severe negative emotions as less helpful.
INTERNATIONAL JOURNAL OF CONTEMPORARY HOSPITALITY MANAGEMENT
(2023)
Review
Management
Minwoo Lee, Young Ho Song, Lin Li, Kyung Young Lee, Sung-Byung Yang
Summary: This study offers a methodological approach using AI-based supervised ML algorithms to detect fake reviews on online platforms. The findings reveal that the random forest algorithm performs the best among seven ML algorithms, with time distance being the most critical determinant of fake reviews.
SERVICE INDUSTRIES JOURNAL
(2022)
Article
Computer Science, Information Systems
Nohel Zaman, David M. Goldberg, Richard J. Gruss, Alan S. Abrahams, Siriporn Srisawas, Peter Ractham, Michelle M. H. Seref
Summary: The paper evaluates various methods for detecting defect-related discussion in online reviews and finds that supervised learning techniques outperform other text analytic techniques in cross-category analysis, especially when confined to a single category of study.
INFORMATION SYSTEMS FRONTIERS
(2022)
Article
Information Science & Library Science
Akriti Chaubey, Chandan Kumar Sahoo
Summary: Although business intelligence is considered a game-changer during the pandemic crisis, many organizations struggle to fully assimilate it. The commitment of top leaders significantly influences the acceptance and routinization of business intelligence within organizations.
INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT
(2021)
Article
Polymer Science
Vanessa Garcia-Martinez, Maria R. Gude, Silvia Calvo, Alejandro Urena
Summary: By introducing two different contents of graphene nanoplatelets into benzoxazine resin, a polymeric nanocomposite with multifunctional properties including high electrical and thermal conductivity was obtained. The nanocomposites showed improved mechanical and thermo-mechanical properties, as well as increased electrical and thermal conductivities. Additionally, the barrier properties of the resin were enhanced by the addition of graphene nanoplatelets.
Article
Thermodynamics
S. G. Castillo-Lopez, C. Villarreal, R. Esquivel-Sirvent, G. Pirruccio
Summary: This theoretical study focuses on the near-field radiative heat transfer between high-T-c superconducting thin films, showing that thin films enhance heat transfer significantly compared to bulk plates. The optical response above and below T-c plays a crucial role, with the superconducting phase transition leading to a suppression of total heat flux.
INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER
(2022)
Review
Operations Research & Management Science
Sharan Srinivas, Surya Ramachandiran
Summary: In the fiercely competitive airline industry, carriers aim to increase customer satisfaction by understanding expectations and personalizing service offerings. With the increasing use of social media, airlines can leverage online customer reviews to gain insights about their services and competitors. This study proposes a framework to automatically extract airline-specific intelligence from online customer reviews, using topic modeling, sentiment analysis, and collocation analysis. The proposed framework achieves higher classification accuracy compared to benchmark models and provides valuable insights about different aspects of airline service quality and reasons for passenger satisfaction/dissatisfaction.
ANNALS OF OPERATIONS RESEARCH
(2023)
News Item
Multidisciplinary Sciences
Katharine Sanderson
Summary: Bailouts ensure the safety of customers' deposits, but the bankruptcy of the bank has raised concerns about future investments in small tech companies.
Article
Green & Sustainable Science & Technology
Simona Margheritti, Andrea Gragnano, Raffaella Villa, Michele Invernizzi, Marco Ghetti, Massimo Miglioretti
Summary: The COVID-19 crisis has caused significant changes in the way people work and has created new emotional strains on workers. This qualitative study aims to explore the emotions expressed by business leaders during the crisis, how they manage their own emotions, and how they manage emotions shown by employees in their company.
Article
Green & Sustainable Science & Technology
Abdulmajeed Alqhatani, Muhammad Shoaib Ashraf, Javed Ferzund, Ahmad Shaf, Hamad Ali Abosaq, Saifur Rahman, Muhammad Irfan, Samar M. Alqhtani
Summary: Business owners and managers need strategic information for decision making, which can be achieved through a hybrid mechanism of business intelligence and machine learning. This mechanism supports organization-wide analysis, provides descriptive and diagnostic analysis, and predicts performance and potential opportunities.
Article
Computer Science, Information Systems
Jianwen Wang, Abdullah Hisam Omar, Fahad M. Alotaibi, Yousef Ibrahim Daradkeh, Sara A. Althubiti
Summary: This study aims to improve organizational performance in privately held companies through the use of business intelligence (BI) and artificial intelligence (AI) in data mining systems (DMS). The results indicate that BI capabilities and reliability have an impact on performance capabilities, and performance evaluation capabilities (PEC) impact competitive advantage. Additionally, the integration of BI infrastructure and BI reliability affects performance capabilities. Innovation also plays a significant role in improving company performance.
INFORMATION PROCESSING & MANAGEMENT
(2022)
Article
Computer Science, Artificial Intelligence
Javier Torregrosa, Sergio D'Antonio-Maceiras, Guillermo Villar-Rodriguez, Amir Hussain, Erik Cambria, David Camacho
Summary: Political tensions have increased in Europe since the beginning of the new century, leading to social movements and political changes in various countries. This study examines the political discourse and underlying tensions during Madrid's elections in May 2021, using a mixed methodology approach. The findings suggest that the electoral campaign is not as negative as perceived by the citizens, and that ideologically extreme parties tend to use more aggressive language.
COGNITIVE COMPUTATION
(2023)
Article
Computer Science, Artificial Intelligence
Wei Sun, Shaoxiong Ji, Erik Cambria, Pekka Marttinen
Summary: Human coders assign standardized medical codes to clinical documents, but it is prone to errors and requires significant effort. Automated medical coding methods using machine learning, such as deep neural networks, have been developed. However, challenges still exist due to code association complexity, noise in lengthy documents, and imbalanced class problem. In this study, we propose a novel neural network model called the Multitask Balanced and Recalibrated Neural Network to address these issues. Experiments on a real-world clinical dataset called MIMIC-III demonstrate that our model outperforms competitive baselines.
ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY
(2023)
Article
Computer Science, Artificial Intelligence
Qika Lin, Rui Mao, Jun Liu, Fangzhi Xu, Erik Cambria
Summary: Knowledge graph completion (KGC) is crucial for many downstream applications. Existing language model-based methods for KGC often overlook the importance of modeling the deeper semantic information, such as topology contexts and logical rules. In this paper, we propose a unified framework FTL-LM that effectively incorporates topology contexts and logical rules in language models, and experimental results demonstrate its superiority over the state-of-the-art methods.
INFORMATION FUSION
(2023)
Article
Computer Science, Information Systems
Erik Cambria, Lorenzo Malandri, Fabio Mercorio, Mario Mezzanzanica, Navid Nobani
Summary: The field of explainable artificial intelligence (XAI) has gained increasing importance in recent years. However, existing research often overlooks the role of natural language in generating explanations. This survey reviews 70 XAI papers published between 2006 and 2021 and evaluates their readiness in terms of natural language explanations. The results show that only a few recent studies have considered using natural language for communication with end users or implemented methods for generating natural language explanations.
INFORMATION PROCESSING & MANAGEMENT
(2023)
Article
Computer Science, Cybernetics
Luna Ansari, Shaoxiong Ji, Qian Chen, Erik Cambria
Summary: Changes in human lifestyle have led to an increase in depression cases. Automated detection methods are effective in identifying depressed individuals. Ensemble models outperform hybrid models for depression detection.
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Ruicheng Liu, Rui Mao, Anh Tuan Luu, Erik Cambria
Summary: The task of resolving repeated objects in natural languages, known as coreference resolution, is an important part of modern natural language processing. It is classified into entity coreference resolution and event coreference resolution based on the resolved objects. Predicting coreference connections and identifying mentions/triggers are the major challenges in coreference resolution due to the difficulty of implicit relationships in natural language understanding. In this survey, we review the current employed evaluation metrics, datasets, and methods, investigating 10 widely used metrics, 18 datasets, and 4 main technical trends. We believe that this work provides a comprehensive roadmap for understanding the past and the future of coreference resolution.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Computer Science, Artificial Intelligence
Seham Basabain, Erik Cambria, Khalid Alomar, Amir Hussain
Summary: An increasing number of studies are using pre-trained language models to tackle few/zero-shot text classification problems. However, most of these studies fail to consider the semantic information embedded in the natural language class labels. This work demonstrates how label information can be leveraged to enhance feature representation in input texts, particularly in scenarios with scarce data resources and short texts lacking semantic information like tweets. The study also shows the effectiveness of zero-shot implementation in predicting new classes across different domains, achieving high accuracy in Arabic sarcasm detection.
Article
Computer Science, Artificial Intelligence
Kai He, Yucheng Huang, Rui Mao, Tieliang Gong, Chen Li, Erik Cambria
Summary: This paper proposes a virtual prompt pre-training method that incorporates the virtual prompt into PLM parameters to achieve entity-relation-aware pre-training. The proposed method provides robust initialization for prompt encoding and avoids the labor-intensive and subjective issues in label word mapping and prompt template engineering.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Zhaoxia Wang, Zhenda Hu, Seng-Beng Ho, Erik Cambria, Ah-Hwee Tan
Summary: This paper proposes a new explainable fine-grained multi-class sentiment analysis method called MiMuSA, which mimics human language understanding processes. It builds multiple knowledge bases to support sentiment understanding and can identify fine-grained multi-class sentiments. Experimental results show that MiMuSA outperforms other existing multi-class sentiment analysis methods in terms of accuracy and F1-Score.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Zhaoxia Wang, Zhenda Hu, Fang Li, Seng-Beng Ho, Erik Cambria
Summary: This research proposes a 3-phase hybrid model that utilizes both technical indicators and social media text sentiments as influence factors for stock trending prediction. The result shows that the proposed method has an accuracy of 73.41% and F1-score of 84.19%. The research not only demonstrates the merits of the proposed method, but also indicates that integrating social opinions with technical indicators is a right direction for enhancing the performance of learning-based stock market trending analysis methods.
COGNITIVE COMPUTATION
(2023)
Article
Computer Science, Artificial Intelligence
Mostafa M. Amin, Erik Cambria, Bjoern W. Schuller
Summary: The employment of foundation models is expanding and ChatGPT has the potential to enhance existing NLP techniques with its novel knowledge.
IEEE INTELLIGENT SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Wei Li, Yang Li, Vlad Pandelea, Mengshi Ge, Luyao Zhu, Erik Cambria
Summary: The paper introduces a new task called emotion-cause pair extraction in conversations (ECPEC), which aims to extract pairs of emotional utterances and corresponding cause utterances in conversations. The utterance-level ECPEC task is more challenging as the distance between emotion and cause utterances is greater. The experimental results on the proposed ConvECPE dataset demonstrate the feasibility of the ECPEC task and the effectiveness of the framework.
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Dazhi Jiang, Runguo Wei, Jintao Wen, Geng Tu, Erik Cambria
Summary: Emotion recognition in conversations has wide applications in various fields. We propose an AutoML strategy based on emotion congruent effect to select suitable knowledge and models, and effectively capture context information and enhance external knowledge in conversations.
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Tan Yue, Rui Mao, Heng Wang, Zonghai Hu, Erik Cambria
Summary: Sarcasm detection is a challenging task in natural language processing, especially in the context of social media where sarcasm is prevalent. This paper proposes a novel model called KnowleNet that incorporates prior knowledge and cross-modal semantic contrast for multimodal sarcasm detection. By leveraging the ConceptNet knowledge base and utilizing contrastive learning, the model achieves state-of-the-art performance on benchmark datasets.
INFORMATION FUSION
(2023)
Article
Computer Science, Artificial Intelligence
Deeksha Varshney, Asif Ekbal, Erik Cambria
Summary: This paper focuses on the neural-based interactive dialogue system that aims to engage and retain humans in long-lasting conversations. It proposes a new neural generative model that combines step-wise co-attention, self-attention-based transformer network, and an emotion classifier to control emotion and knowledge transfer during response generation. The results from quantitative, qualitative, and human evaluation show that the proposed models can generate natural and coherent sentences, capturing essential facts with significant improvement over emotional content.
KNOWLEDGE-BASED SYSTEMS
(2024)
