Article
Computer Science, Artificial Intelligence
Zoltan Geler, Milos Savic, Brankica Bratic, Vladimir Kurbalija, Mirjana Ivanovic, Weihui Dai
Summary: This research focuses on ML-based sentiment analysis of food services reviews, comparing regression models to predict customer satisfaction. Keywords extracted from customer reviews have potential for predicting satisfaction in food taste, service, and environment aspects.
CONNECTION SCIENCE
(2021)
Article
Computer Science, Artificial Intelligence
Tiankuo Li, Hongji Xu, Zhi Liu, Zheng Dong, Qiang Liu, Juan Li, Shidi Fan, Xiaojie Sun
Summary: With the rapid development of Internet technology and the explosive growth of digital text, opinion mining has become an important research hotspot in the field of natural language processing (NLP). This paper proposes a new deep learning framework for opinion mining, which is shown to outperform other algorithms in terms of performance.
Article
Multidisciplinary Sciences
Olusola Olabanjo, Ashiribo Wusu, Oseni Afisi, Mauton Asokere, Rebecca Padonu, Olufemi Olabanjo, Oluwafolake Ojo, Olusegun Folorunso, Benjamin Aribisala, Manuel Mazzara
Summary: This study designed a Natural Language Processing framework to understand the Nigerian 2023 presidential election based on public opinion using Twitter dataset. Sentiment analysis was performed using three machine learning models and the results showed that the BERT model had the highest accuracy. The study also found that Peter Obi had the highest positive sentiments on Twitter, while Atiku had the highest number of followers, and Tinubu had the highest network of active friends.
Article
Management
Zelin Zhang, Kejia Yang, Jonathan Z. Zhang, Robert W. Palmatier
Summary: Massive online text reviews are a powerful tool for market research, helping firms understand consumer experiences and improve their products and services. This research proposes a novel machine learning framework that can extract consumer opinions and uncover interaction effects among these opinions, identifying potential areas for improvement. The framework successfully identifies synergistic and dysergistic effects in large-scale customer ratings and text reviews for hotels, providing valuable managerial insights.
MANAGEMENT SCIENCE
(2023)
Article
Computer Science, Artificial Intelligence
Rahul Kumar Singh, Manoj Kumar Sachan, R. B. Patel
Summary: Sentiment analysis has gained significant attention from researchers worldwide in the field of natural language processing and text mining. The survey examines different aspects of sentiment analysis, such as classification, tasks, and algorithms, to provide support for future research.
ARTIFICIAL INTELLIGENCE REVIEW
(2021)
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)
Article
Communication
Mazen El-Masri, Allan Ramsay, Hanady Mansour Ahmed, Tariq Ahmad
Summary: The study analyzes the emotional responses of Qatar residents during a blockade enforced by neighboring countries, finding that they used positive emotions like love and optimism to cope with adversities and accompanying emotions of fear and anger. Furthermore, their adaptive resilient capacities gradually strengthened during the nine months of blockade, supporting the renowned theory of positive emotions using an advanced methodology and a large-scale dataset.
INFORMATION COMMUNICATION & SOCIETY
(2021)
Review
Computer Science, Software Engineering
Adailton F. Araujo, Marcos P. S. Golo, Ricardo M. Marcacini
Summary: This paper investigates opinion mining for app reviews by comparing different textual representation techniques for classification, sentiment analysis, and utility prediction. The findings show that the traditional Bag-of-Words model is competitive with Neural Language models (NLM) in these tasks, but NLM proves to be more advantageous in terms of dimensionality reduction and handling semantic proximity between review texts.
AUTOMATED SOFTWARE ENGINEERING
(2022)
Article
Computer Science, Software Engineering
Walid Cherif, Abdellah Madani, Mohamed Kissi
Summary: The article discusses the application of machine learning techniques in text data classification, introducing a new classification approach and highlighting its advantages. Experimental results demonstrate that the method performs well in automatic text categorization.
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
(2022)
Article
Environmental Sciences
Sherif Ahmed Abu El-Magd, Ismael S. Ismael, Mohamed A. Sh. El-Sabri, Mohamed Sayed Abdo, Hassan I. Farhat
Summary: A machine learning model, SVM integrated with WQI, was used to assess groundwater quality in arid areas. The SVM-WQI model achieved high accuracy (0.90 accuracy) in assessing groundwater quality, which can be helpful for future development in such areas.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2023)
Article
Computer Science, Information Systems
Marichi Gupta, Aditya Bansal, Bhav Jain, Jillian Rochelle, Atharv Oak, Mohammad S. Jalali
Summary: The study explores the controversial topic of weather's impact on COVID-19 transmission, analyzing Twitter users' evolving perceptions over time. Results show a lack of consensus among the public, with a shift towards more tweets claiming some effect of weather as the pandemic progressed. This research approach effectively measures population perceptions and identifies misconceptions that can inform public health communications.
INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS
(2021)
Article
Computer Science, Artificial Intelligence
Cem Rifki Aydin, Tunga Gungor
Summary: An unsupervised and semi-supervised sentiment analysis method based on antonym word pairs as seeds was developed for Turkish, achieving significant improvements over existing methods. The combination of unsupervised and supervised approaches outperformed other methods, showing the portability of the approaches across languages. Comprehensive analysis of supervised methods and ensemble of classifiers were also conducted to enhance the sentiment analysis results.
NATURAL LANGUAGE ENGINEERING
(2021)
Article
Construction & Building Technology
Bibo Dai, Zhijun Xu, Jie Zeng, Yousef Zandi, Abouzar Rahimi, Sara Pourkhorshidi, Mohamed Amine Khadimallah, Xingdong Zhao, Islam Ezz El-Arab
Summary: Mining subsidence causing surface subsidence affects neighboring structures and utilities, with the findings emphasizing the importance of maintaining an appropriate factor of safety for the moment-rotation response of structures.
STEEL AND COMPOSITE STRUCTURES
(2021)
Article
Biochemical Research Methods
Yufeng Liu, Shuyu Wang, Xiang Li, Yinbo Liu, Xiaolei Zhu
Summary: Neuropeptides play critical roles in physiological processes and diseases. A new model, NeuroPpred-SVM, is proposed to predict neuropeptides based on embeddings and sequential features using a support vector machine. Experimental results show that NeuroPpred-SVM outperforms existing models in terms of accuracy and specificity.
JOURNAL OF PROTEOME RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Jianjiang Li, Jinliang Shi, Zhiguo Liu, Can Feng
Summary: Support Vector Machine (SVM) is a powerful machine learning algorithm widely used in various fields. However, its efficiency decreases when dealing with large datasets. To address this issue, a parallel balanced SVM algorithm called PB-SVM is proposed based on Spark. PB-SVM solves the data skew and support vector difference problems of Cascade SVM. Experimental results demonstrate that PB-SVM significantly improves efficiency and accuracy compared to MLlib-SVM and Cascade SVM on Spark.
INTELLIGENT DATA ANALYSIS
(2023)
Article
Computer Science, Artificial Intelligence
Salud Maria Jimenez-Zafra, Noa P. Cruz-Diaz, Maite Taboada, Maria Teresa Martin-Valdivia
Summary: Accurate negation identification is crucial in sentiment analysis, and recent developments have provided methods for accurate detection in languages other than English. This paper focuses on implementing a Spanish system for negation cue detection and applying it to sentiment analysis, demonstrating improvements in accuracy for Spanish sentiment analysis tasks.
NATURAL LANGUAGE ENGINEERING
(2021)
Article
Computer Science, Interdisciplinary Applications
Pilar Lopez-Ubeda, Manuel Carlos Diaz-Galiano, Teodoro Martin-Noguerol, Antonio Luna, L. Alfonso Urena-Lopez, M. Teresa Martin-Valdivia
Summary: This study developed machine learning classification models based on NLP using patient data from radiological reports and protocols. The best proposed system achieved 92.2% accuracy in the CT dataset and 86.9% in the MRI dataset. The potential efficiency, quality, and cost-effectiveness make the best machine learning system currently used in real scenarios by radiologists as a decision support tool for protocol assignment in CT and MRI studies.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2021)
Article
Computer Science, Artificial Intelligence
Flor Miriam Plaza-del-Arco, M. Dolores Molina-Gonzalez, L. Alfonso Urena-Lopez, M. Teresa Martin-Valdivia
Summary: The paper discusses the task of Spanish hate speech identification on social media and the capabilities of new techniques based on machine learning. The study compares the performance of different methods, with the main contribution being the achievement of promising results in Spanish through the application of multilingual and monolingual pre-trained language models.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Chemistry, Multidisciplinary
Pilar Lopez-Ubeda, Flor Miriam Plaza-del-Arco, Manuel Carlos Diaz-Galiano, Maria-Teresa Martin-Valdivia
Summary: Anorexia is a mental disorder involving abnormal nutritional intake behaviors, with early identification and appropriate treatment improving recovery speed. Social media use is strongly associated with eating concerns, and Natural Language Processing can aid in early anorexia detection in textual data. Transfer learning techniques, particularly using Transformer-based models, show promise in detecting anorexia in Spanish tweets.
APPLIED SCIENCES-BASEL
(2021)
Article
Chemistry, Multidisciplinary
Ismael Garrido-Munoz, Arturo Montejo-Raez, Fernando Martinez-Santiago, L. Alfonso Urena-Lopez
Summary: Deep neural networks play a dominant role in various machine learning fields, including natural language processing, by continuously releasing versatile and performing models. However, the potential biases, inconsistencies, and flaws inherited from training collections pose challenges in handling bias in deep NLP. Researchers are actively exploring methods and strategies to address bias in deep NLP.
APPLIED SCIENCES-BASEL
(2021)
Article
Multidisciplinary Sciences
Salud Maria Jimenez-Zafra, Antonio Jose Saez-Castillo, Antonio Conde-Sanchez, Maria Teresa Martin-Valdivia
Summary: The study found that using negativity in tweets can increase the likelihood of retweeting, and the iSOL lexicon is the most effective in determining the relationship between polarity and virality.
ROYAL SOCIETY OPEN SCIENCE
(2021)
Article
Computer Science, Information Systems
Jenny A. Ortiz-Zambrano, Cesar Espin-Riofrio, Arturo Montejo-Raez
Summary: This study explores the combination of traditional linguistic features and neural encodings to predict lexical complexity in texts, finding that linguistic features can improve the performance of deep learning systems.
Article
Biology
Mariia Chizhikova, Pilar Lopez-Ubeda, Jaime Collado-Montanez, Teodoro Martin-Noguerol, Manuel C. Diaz-Galiano, Antonio Luna, L. Alfonso Urena-Lopez, M. Teresa Martin-Valdivia
Summary: This paper introduces a new corpus of radiology medical reports written in Spanish and labeled with ICD-10. It is a high-quality corpus manually labeled and reviewed by radiologists that is freely available for the research community. The corpus was used to conduct an experimental approach using machine learning algorithms, which showed promising results with high f1-scores.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Computer Science, Cybernetics
Alejandro A. Torres-Garcia, Fernando Martinez-Santiago, Arturo Montejo-Raez, L. Alfonso Urena-Lopez
Summary: In this study, the correlation between text complexity and reading comprehension was explored using the perspective of neuroinformatics. By analyzing EEG signals, this research revealed the potential to infer the difficulty level of a text based on brain activity. The results demonstrated that the deep learning model EEGNet outperformed previous methods in classifying the complexity of texts.
INTERNATIONAL JOURNAL OF HUMAN-COMPUTER INTERACTION
(2023)
Article
Linguistics
Jose Antonio Garcia-Diaz, Salud Maria Jimenez-Zafra, Maria-Teresa Martin Valdivia, Francisco Garcia-Sanchez, L. Alfonso Urena-Lopez, Rafael Valencia-Garcia
Summary: This paper presents the PoliticEs 2022 shared task, which aims to extract political ideology from a user's set of tweets. A total of 63 teams participated, with 20 teams submitting results. Most teams adopted Transformer-based approaches.
PROCESAMIENTO DEL LENGUAJE NATURAL
(2022)
Article
Linguistics
Flor Miriam Plaza-del-Arco, Salud Maria Jimenez-Zafra, Arturo Montejo-Raez, M. Dolores Molina-Gonzalez, L. Alfonso Urena-Lopez, M. Teresa Martin-Valdivia
Summary: The EmoEvalEs shared task at IberLEF 2021 aims to promote emotion detection and evaluation for Spanish, involving fine-grained emotion classification of tweets. With 70 teams registering and 15 submitting results in this edition, most teams utilized neural networks with Transformers being the most popular model. Additionally, some teams also incorporated features of offensiveness and event from the corpus in their analysis.
PROCESAMIENTO DEL LENGUAJE NATURAL
(2021)
Article
Computer Science, Information Systems
Flor Miriam Plaza-Del-Arco, M. Dolores Molina-Gonzalez, L. Alfonso Urena-Lopez, Maria Teresa Martin-Valdivia
Summary: The rise of social media has changed communication, but also led to an increase in inappropriate behavior such as hate speech. Automatic methods are crucial for detecting such content due to the large amount of data posted daily. The Natural Language Processing community is increasingly testing various techniques for hate speech detection.
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)
