Review
Business
Petr Hajek, Jean-Michel Sahut
Summary: Online reviews are increasingly recognized as a key source of information influencing consumer behavior, particularly in industries such as restaurants. Recent studies suggest that both the semantic meaning and sentiment of reviews are crucial for detecting fake reviews, and recommend integrating content analysis with reviewer behavior.
TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
(2022)
Review
Food Science & Technology
Guang Tian, Liang Lu, Christopher McIntosh
Summary: By analyzing Yelp restaurant review data, it was found that consumers tend to use more positive sentiment words in their reviews, which are positively related to ratings. Consumers used more sentiment words when discussing service, but the least when discussing social-related topics.
FOOD QUALITY AND PREFERENCE
(2021)
Review
Computer Science, Artificial Intelligence
Wei Zhenlin, Wang Chuantao, Yang Xuexin
Summary: Sentiment classification aims to automatically judge the sentiment tendency of text. Traditional deep learning models for online review sentiment classification require a large number of manually annotated samples, which is worrisome given the massive online review data. Additionally, the traditional model overlooks the imbalanced distribution of classification samples, resulting in a decline in performance. To address these challenges, a weak tagging and imbalanced network is constructed, achieving significantly better results in the sentiment classification of hotel review data compared to traditional models.
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2023)
Review
Computer Science, Hardware & Architecture
Mayur Wankhade, Chandra Sekhara Rao Annavarapu, Mukul Kirti Verma
Summary: With the rapid growth of e-commerce, the number of online product reviews has significantly increased, with historical reviews influencing buyers' decisions despite sentiment expression variations. The neglect of sentiment information in word vectors has led to the proposal of the CBVoSD algorithm to improve sentiment classification accuracy.
JOURNAL OF SUPERCOMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Marouane Birjali, Mohammed Kasri, Abderrahim Beni-Hssane
Summary: Sentiment analysis, also known as Opinion Mining, is the task of extracting and analyzing people's opinions and emotions towards different entities. It is a powerful tool used by businesses, governments, and researchers to gain insights and make better decisions. This paper provides a comprehensive study of sentiment analysis methods, challenges, and trends for researchers in the field.
KNOWLEDGE-BASED SYSTEMS
(2021)
Review
Computer Science, Information Systems
Praphula Kumar Jain, Rajendra Pamula, Gautam Srivastava
Summary: Consumer sentiment analysis is a recent trend in social media-related applications, and machine learning techniques have been utilized to address the challenge of processing a large amount of social web data. This paper presents a systematic literature review to explore the future research directions and implications for service providers and researchers in the domain of hospitality and tourism.
COMPUTER SCIENCE REVIEW
(2021)
Article
Hospitality, Leisure, Sport & Tourism
Yi Luo, Xiaowei Xu
Summary: During the COVID-19 pandemic, online reviews are crucial for customers to make safe dining decisions. This study identified four key restaurant features cared for by customers and compared the performance of deep learning and traditional machine learning algorithms in sentiment analysis and review rating prediction.
INTERNATIONAL JOURNAL OF HOSPITALITY MANAGEMENT
(2021)
Review
Hospitality, Leisure, Sport & Tourism
Jun Liu, Sike Hu, Fuad Mehraliyev, Haiyue Zhou, Yunyun Yu, Luyu Yang
Summary: This study aims to establish a model for rapid and accurate emotion recognition in restaurant online reviews, advancing the literature and providing practical insights into electronic word-of-mouth management for the industry. The hybrid model proposed in this study, which integrates deep learning and a sentiment lexicon, performs well in emotion recognition and is highly applicable for mining online reviews in a restaurant setting.
INTERNATIONAL JOURNAL OF CONTEMPORARY HOSPITALITY MANAGEMENT
(2023)
Review
Computer Science, Artificial Intelligence
Nora Alturayeif, Hamzah Luqman, Moataz Ahmed
Summary: This paper reviews different techniques for stance detection and analyzes the application of machine learning models in this field. The analysis reveals that deep learning models with self-attention mechanism are widely used, and emerging techniques like few-shot learning and multitask learning have been extensively applied. However, the application of these models in real-world scenarios is still limited.
NEURAL COMPUTING & APPLICATIONS
(2023)
Review
Clinical Neurology
Justin E. Tang, Varun Arvind, Calista Dominy, Christopher A. White, Samuel K. Cho, Jun S. Kim
Summary: This study analyzes online written reviews of spine surgeons and explores the biases associated with demographic factors and trends in words utilized. The results show that gender and age have a significant impact on sentiment analysis scores. The best and worst reviewed surgeons are mainly evaluated based on behavioral factors and pain. The use of certain clinically relevant words affects the odds of a positive review. Therefore, establishing proper pain expectations prior to any intervention is crucial.
GLOBAL SPINE JOURNAL
(2023)
Review
Computer Science, Artificial Intelligence
Tej Bahadur Shahi, Chiranjibi Sitaula
Summary: This study surveys various natural language processing research works and associated resources in the Nepali language, organizing the NLP methods, techniques, and application tasks using a comprehensive taxonomy. Discussions and analyses based on this information aim to further enhance NLP research in the Nepali language, providing researchers with detailed backgrounds and motivations to explore new avenues and advancements.
ARTIFICIAL INTELLIGENCE REVIEW
(2022)
Review
Computer Science, Artificial Intelligence
Naila Aslam, Kewen Xia, Furqan Rustam, Ernesto Lee, Imran Ashraf
Summary: Online meeting apps have become a potential solution for conferences, education, and meetings during the COVID-19 outbreak. This study proposes a novel self voting classification approach for sentiment analysis, which improves the performance of traditional machine learning models. The experiments show that the proposed approach achieves high accuracy scores using different criteria.
PEERJ COMPUTER SCIENCE
(2022)
Article
Multidisciplinary Sciences
Ahmed Omar, Tarek Abd El-Hafeez
Summary: This paper presents a comparative study of quantum computing and machine learning for Arabic language document classification. The results show that quantum computing slightly outperforms classic machine learning in sentiment analysis of Arabic tweets and has faster processing times for larger datasets. Additionally, classic machine learning achieves higher accuracy when dealing with smaller datasets.
SCIENTIFIC REPORTS
(2023)
Review
Computer Science, Artificial Intelligence
Minghui Huang, Haoran Xie, Yanghui Rao, Yuwei Liu, Leonard K. M. Poon, Fu Lee Wang
Summary: With the availability and popularity of sentiment-rich resources, new opportunities and challenges have emerged in sentiment analysis. Previous studies have either ignored contextual information of sentences or not considered sentiment information embedded in sentiment words. To address these limitations, we propose a new model, called Sentiment Convolutional Neural Network (SentiCNN), which combines contextual and sentiment information to analyze the sentiments of sentences.
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
(2022)
Article
Chemistry, Multidisciplinary
Mohammed Hadwan, Mohammed Al-Sarem, Faisal Saeed, Mohammed A. Al-Hagery
Summary: Analyzing the sentiment of Arabic texts is a significant research challenge. Existing studies on Arabic sentiment analysis have focused on Twitter data while neglecting the reviews on Google Play or the App Store. This paper aims to analyze user opinions of six healthcare applications and proposes an improved sentiment classification approach using machine learning models.
APPLIED SCIENCES-BASEL
(2022)
Review
Computer Science, Artificial Intelligence
Wei Gao, Shuangshuang Ge
Summary: This study provides a comprehensive review of slope stability research based on artificial intelligence methods, focusing on slope stability computation and evaluation. The review covers studies using quasi-physical intelligence methods, simulated evolutionary methods, swarm intelligence methods, hybrid intelligence methods, artificial neural network methods, vector machine methods, and other intelligence methods. The merits, demerits, and state-of-the-art research advancement of these studies are analyzed, and possible research directions for slope stability investigation based on artificial intelligence methods are suggested.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Khuong Le Nguyen, Hoa Thi Trinh, Saeed Banihashemi, Thong M. Pham
Summary: This study investigated the influence of input parameters on the shear strength of RC squat walls and found that ensemble learning models, particularly XGBoost, can effectively predict the shear strength. The axial load had a greater influence than reinforcement ratio, and longitudinal reinforcement had a more significant impact compared to horizontal and vertical reinforcement. The performance of XGBoost model outperforms traditional design models and reducing input features still yields reliable predictions.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Bo Hu, Huiyan Zhang, Xiaoyi Wang, Li Wang, Jiping Xu, Qian Sun, Zhiyao Zhao, Lei Zhang
Summary: A deep hierarchical echo state network (DHESN) is proposed to address the limitations of shallow coupled structures. By using transfer entropy, candidate variables with strong causal relationships are selected and a hierarchical reservoir structure is established to improve prediction accuracy. Simulation results demonstrate that DHESN performs well in predicting algal bloom.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Limin Wang, Lingling Li, Qilong Li, Kuo Li
Summary: This paper discusses the urgency of learning complex multivariate probability distributions due to the increase in data variability and quantity. It introduces a highly scalable classifier called TAN, which utilizes maximum weighted spanning tree (MWST) for graphical modeling. The paper theoretically proves the feasibility of extending one-dependence MWST to model high-dependence relationships and proposes a heuristic search strategy to improve the fitness of the extended topology to data. Experimental results demonstrate that this algorithm achieves a good bias-variance tradeoff and competitive classification performance compared to other high-dependence or ensemble learning algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhejing Hu, Gong Chen, Yan Liu, Xiao Ma, Nianhong Guan, Xiaoying Wang
Summary: Anxiety is a prevalent issue and music therapy has been found effective in reducing anxiety. To meet the diverse needs of individuals, a novel model called the spatio-temporal therapeutic music transfer model (StTMTM) is proposed.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Nur Ezlin Zamri, Mohd. Asyraf Mansor, Mohd Shareduwan Mohd Kasihmuddin, Siti Syatirah Sidik, Alyaa Alway, Nurul Atiqah Romli, Yueling Guo, Siti Zulaikha Mohd Jamaludin
Summary: In this study, a hybrid logic mining model was proposed by combining the logic mining approach with the Modified Niche Genetic Algorithm. This model improves the generalizability and storage capacity of the retrieved induced logic. Various modifications were made to address other issues. Experimental results demonstrate that the proposed model outperforms baseline methods in terms of accuracy, precision, specificity, and correlation coefficient.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
David Jacob Kedziora, Tien-Dung Nguyen, Katarzyna Musial, Bogdan Gabrys
Summary: The paper addresses the problem of efficiently optimizing machine learning solutions by reducing the configuration space of ML pipelines and leveraging historical performance. The experiments conducted show that opportunistic/systematic meta-knowledge can improve ML outcomes, and configuration-space culling is optimal when balanced. The utility and impact of meta-knowledge depend on various factors and are crucial for generating informative meta-knowledge bases.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
G. Sophia Jasmine, Rajasekaran Stanislaus, N. Manoj Kumar, Thangamuthu Logeswaran
Summary: In the context of a rapidly expanding electric vehicle market, this research investigates the ideal locations for EV charging stations and capacitors in power grids to enhance voltage stability and reduce power losses. A hybrid approach combining the Fire Hawk Optimizer and Spiking Neural Network is proposed, which shows promising results in improving system performance. The optimization approach has the potential to enhance the stability and efficiency of electric grids.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhijiang Wu, Guofeng Ma
Summary: This study proposes a natural language processing-based framework for requirement retrieval and document association, which can help to mine and retrieve documents related to project managers' requirements. The framework analyzes the ontology relevance and emotional preference of requirements. The results show that the framework performs well in terms of iterations and threshold, and there is a significant matching between the retrieved documents and the requirements, which has significant managerial implications for construction safety management.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Yung-Kuan Chan, Chuen-Horng Lin, Yuan-Rong Ben, Ching-Lin Wang, Shu-Chun Yang, Meng-Hsiun Tsai, Shyr-Shen Yu
Summary: This study proposes a novel method for dog identification using nose-print recognition, which can be applied to controlling stray dogs, locating lost pets, and pet insurance verification. The method achieves high recognition accuracy through two-stage segmentation and feature extraction using a genetic algorithm.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Shaohua Song, Elena Tappia, Guang Song, Xianliang Shi, T. C. E. Cheng
Summary: This study aims to optimize supplier selection and demand allocation decisions for omni-channel retailers in order to achieve supply chain resilience. It proposes a two-phase approach that takes into account various factors such as supplier evaluation and demand allocation.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jinyan Hu, Yanping Jiang
Summary: This paper examines the allocation problem of shared parking spaces considering parking unpunctuality and no-shows. It proposes an effective approach using sample average approximation (SAA) combined with an accelerating Benders decomposition (ABD) algorithm to solve the problem. The numerical experiments demonstrate the significance of supply-demand balance for the operation and user satisfaction of the shared parking system.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Soroor Motie, Bijan Raahemi
Summary: Financial fraud is a persistent problem in the finance industry, but Graph Neural Networks (GNNs) have emerged as a powerful tool for detecting fraudulent activities. This systematic review provides a comprehensive overview of the current state-of-the-art technologies in using GNNs for financial fraud detection, identifies gaps and limitations in existing research, and suggests potential directions for future research.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Enhao Ning, Changshuo Wang, Huang Zhang, Xin Ning, Prayag Tiwari
Summary: This review provides a detailed overview of occluded person re-identification methods and conducts a systematic analysis and comparison of existing deep learning-based approaches. It offers important theoretical and practical references for future research in the field.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jiajun Ma, Songyu Hu, Jianzhong Fu, Gui Chen
Summary: The article presents a novel visual hierarchical attention detector for multi-scale defect location and classification, utilizing texture, semantic, and instance features of defects through a hierarchical attention mechanism, achieving multi-scale defect detection in bearing images with complex backgrounds.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)