Article
Computer Science, Artificial Intelligence
Baris Ozyurt, M. Ali Akcayol
Summary: With the widespread use of social networks and other platforms, the volume of user-generated textual data is growing rapidly, making sentiment analysis and opinion mining in user reviews more and more important. To tackle issues like data sparsity and lack of co-occurrence patterns, studies have proposed methods like SS-LDA to adapt LDA for short texts. Experimental results indicate that SS-LDA performs competitively in extracting product aspects.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Jianfei Yu, Kai Chen, Rui Xia
Summary: The study proposes a general Hierarchical Interactive Multimodal Transformer (HIMT) model to address the shortcomings in aspect-based multimodal sentiment analysis (ABMSA) and achieves significant improvements in experimental results.
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
(2023)
Article
Computer Science, Information Systems
Li Yang, Jin-Cheon Na, Jianfei Yu
Summary: In this paper, we propose a multi-task learning framework called CMMT for End-to-End Multimodal Aspect-Based Sentiment Analysis. Experimental results demonstrate that CMMT consistently outperforms the state-of-the-art approach JML and achieves superior performance in aspect extraction and sentiment classification compared to other systems.
INFORMATION PROCESSING & MANAGEMENT
(2022)
Article
Computer Science, Artificial Intelligence
Giuseppe D'Aniello, Matteo Gaeta, Ilaria La Rocca
Summary: This article presents an overview of techniques and approaches for aspect-based sentiment analysis (ABSA) and highlights the main issues in this field. The KnowMIS-ABSA model is proposed, which emphasizes that sentiment, affect, emotion, and opinion are different concepts and should be measured using different tools and metrics.
ARTIFICIAL INTELLIGENCE REVIEW
(2022)
Article
Computer Science, Information Systems
Azizkhan F. Pathan, Chetana Prakash
Summary: Aspect-based Opinion Mining is a fine-grained Sentiment Analysis method that models the relationship between aspect terms and context words using an Attention-based Bidirectional Long Short-Term Memory network. By incorporating a Sentiment Intensity Lexicon, the proposed framework improves classification accuracy by considering the interaction between aspects and context words.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Eman M. Aboelela, Walaa Gad, Rasha Ismail
Summary: In recent years, many users prefer online shopping, allowing customers to submit comments and feedback on shopping websites. Opinion mining and sentiment analysis are used to assist buyers and sellers in making purchase decisions. A semantic-based aspect level opinion mining (SALOM) model is proposed to consider negation words and other types of product aspects, with promising experimental results.
PEERJ COMPUTER SCIENCE
(2021)
Article
Computer Science, Information Systems
Lili Shang, Meiyun Zuo
Summary: In this study, a fused sequential and hierarchical representation (FSHR) model is proposed for extracting aspect terms from opinionated sentences. The model combines sequential and hierarchical representations to capture both linear semantic information for predicting meaning-related aspect terms and syntactic relations for identifying structure-related aspect terms. Experimental results demonstrate that FSHR outperforms competitive baselines, and further analysis reveals the effectiveness of the model.
JOURNAL OF INFORMATION SCIENCE
(2023)
Article
Computer Science, Artificial Intelligence
Fang Chen, Zhongliang Yang, Yongfeng Huang
Summary: This paper proposes a novel multi-task learning framework for end-to-end aspect sentiment triplet extraction (ASTE). By decomposing ASTE into target tagging, opinion tagging, and sentiment tagging subtasks, and utilizing specific tagging schemes, our framework achieves better performance in extracting overlapping triplets and identifying long-range correspondences.
Article
Computer Science, Theory & Methods
Mohammad Tubishat, Norisma Idris, Mohammad Abushariah
Summary: This study presents a supervised aspect extraction algorithm for explicit aspect extraction from formal and informal texts. By combining 126 aspect extraction rules and improving the Whale Optimization Algorithm (WOA) with a new algorithm called improved WOA (IWOA), the research achieved better results than other baseline works and recent studies.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2021)
Article
Computer Science, Information Systems
Achraf Boumhidi, Abdessamad Benlahbib, El Habib Nfaoui
Summary: With the growth of Internet-based applications, such as social networks and e-commerce websites, generating and processing a large volume of opinions and reviews about products and services becomes crucial. Many systems have been proposed to generate and visualize reputation based on textual and numerical reviews. However, these systems fail to consider the presence of malicious users and lack reputation scores for different aspects of the product. Therefore, a system that incorporates spam filtering, review popularity, review posting time, and aspect-based sentiment analysis has been developed to generate accurate and reliable reputation values.
Review
Computer Science, Information Systems
Salha Alyami, Areej Alhothali, Amani Jamal
Summary: This article presents a systematic literature review on the techniques and resources used for Arabic ABSA. The review covered 47 primary studies published between 2012 and 2021 and analyzed them based on the dataset used, the domain covered, the Arabic language type, preprocessing procedures, selected features, word representation, employed techniques, and evaluation metrics. The analysis revealed various limitations and issues, and suggested several future research directions.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Education & Educational Research
Jing Chen, Ruiqi Wang, Bei Fang, Chen Zuo
Summary: Online learning has grown rapidly with billions of participants, but the high dropout rate and unsatisfactory learning performance persist. However, learners' reviews provide valuable feedback for improvement. Hence, it is necessary to perform fine-grained aspect-based opinion mining on course reviews to analyze feedback.
INTERACTIVE LEARNING ENVIRONMENTS
(2023)
Article
Computer Science, Artificial Intelligence
Vladyslav Matsiiako, Flavius Frasincar, David Boekestijn
Summary: This paper proposes a method called AspEntQuaNet for aspect-based sentiment quantification, which extends the QuaNet deep learning method and improves it by considering aspects, ternary sentiment quantification, and using an entropy-based sorting procedure. The results show that AspEntQuaNet outperforms other existing methods on the SemEval 2016 dataset.
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Estela Saquete, Jose Zubcoff, Yoan Gutierrez, Patricio Martinez-Barco, Javi Fernandez
Summary: The focus of this research is to discover the main features of virality patterns in Twitter. Five trending topics related to the COVID-19 pandemic in Spanish language were selected. Opinion mining techniques were used to structure the information based on message polarity and emotions. Data mining techniques, specifically association rules mining, were then applied to identify the highest viral message patterns and the relevant characteristics of patterns with low impact. The analysis revealed that messages with high-negative polarity and intense emotions, particularly fear, sadness, anger, and surprise intensified by the COVID-19 pandemic, are more likely to go viral on social media. On the other hand, messages with little news coverage, few authors, and the absence of surprise are relevant features for messages with very low dissemination on social media.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Mohammed M. Abdelgwad, Taysir Hassan A. Soliman, Ahmed Taloba, Mohamed Fawzy Farghaly
Summary: Aspect-based Sentiment Analysis (ABSA) achieves a fine-grained analysis of the aspects and sentiments in a document or sentence, with proposed deep learning models showing improved performance in Arabic hotel review datasets.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Chemistry, Analytical
Tauseef Rana, Yawar Abbas Bangash, Abdullah Baz, Toqir Ahmad Rana, Muhammad Ali Imran
Article
Computer Science, Artificial Intelligence
Toqir A. Rana, Yu-N Cheah, Tauseef Rana
APPLIED INTELLIGENCE
(2020)
Article
Computer Science, Artificial Intelligence
Toqir A. Rana, Kiran Shahzadi, Tauseef Rana, Ahsan Arshad, Mohammad Tubishat
Summary: During the last two decades, sentiment analysis has gained significant attention in the fields of Natural Language Processing and data mining. However, Roman Urdu, the third most used language worldwide, has been neglected in sentiment analysis research. This article proposes an unsupervised approach for sentiment analysis in Roman Urdu and demonstrates its effectiveness through experiments.
ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING
(2022)
Article
Mathematical & Computational Biology
Saleem Mustafa, Muhammad Waseem Iqbal, Toqir A. Rana, Arfan Jaffar, Muhammad Shiraz, Muhammad Arif, Samia Allaoua Chelloug
Summary: Malignant melanoma, if left untreated, is one of the deadliest skin diseases. The paper proposes using the active contour method to address the challenge of unclear skin images and utilizes Gaussian filter-based maximum entropy and morphological processing methods to find automatic seed points for segmentation. The methodology shows promising results in the detection of lesions from dermoscopic images.
COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE
(2022)
Review
Chemistry, Multidisciplinary
Syed Faisal Abbas Shah, Muhammad Iqbal, Zeeshan Aziz, Toqir A. Rana, Adnan Khalid, Yu-N Cheah, Muhammad Arif
Summary: This article focuses on the role of IoT devices in smart buildings, highlighting the IoT devices platform and its components. It also discusses security challenges related to IoT and smart buildings, as well as describing the main factors and different methods of using machine learning with IoT technologies to improve the efficiency of smart buildings.
APPLIED SCIENCES-BASEL
(2022)
Review
Engineering, Electrical & Electronic
Muhammad Aqeel, Fahad Ali, Muhammad Waseem Iqbal, Toqir A. Rana, Muhammad Arif, Md. Rabiul Auwul
Summary: The past two decades have seen significant growth in Internet of Things (IoT) applications, with over 50 billion connected devices globally. However, the connectivity of IoT applications with the internet has also made them vulnerable to a range of traditional threats, including viruses, malware, and backdoor attacks. Traditional security techniques are no longer sufficient to address these issues, and there is a need to explore advanced technologies such as blockchain, machine learning, and artificial intelligence to ensure the security and privacy of IoT systems. This study aims to identify and categorize the various types of threats targeting IoT systems and develop recovery mechanisms to mitigate the damage caused by these threats. The findings of this study highlight the research gaps in this area and suggest the importance of expanding advanced technologies to guarantee the security and privacy of IoT systems.
JOURNAL OF SENSORS
(2022)
Article
Computer Science, Information Systems
Tauseef Rana, Ayesha Maqbool, Toqir A. Rana, Alina Mirza, Zeshan Iqbal, Muhammad Attique Khan, Majed Alhaisoni, Abdullah Alqahtani, Ye Jin Kim, Byoungchol Chang
Summary: Industrial 5.0 is focused on constructing new large scale hybrid systems by utilizing existing and new technologies. This paper proposes a new incremental development process using the EX-MAN component model, which is different and beneficial compared to existing approaches in component-based development. The proposed method is evaluated through the construction of a smart ATM system, supported by the Exogenous Composition Framework tool for system modeling and simulation.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Mubashar Hussain, Toqir A. Rana, Aksam Iftikhar, M. Usman Ashraf, Muhammad Waseem Iqbal, Ahmed Alshaflut, Abdullah Alourani
Summary: In sentiment analysis, extracting aspects or opinion targets from user reviews about a product is crucial. Rule-based approaches, such as dependency-based rules and sequential rules, have been widely used for this purpose. However, they heavily rely on the accuracy of POS tagger and dependency parser, and sequential rule-based approaches generally lack generalization capability. In this article, a multi-layered rule-based technique is proposed, which combines syntactic dependency parser based rules and selective sequential rules to extract noun aspects. Additionally, rules for extracting verb aspects are developed based on the association between verb and opinion words. The proposed technique improves the extraction results on two customer review datasets, achieving F1 scores of 0.90 and 0.88, respectively, surpassing existing approaches.
CMC-COMPUTERS MATERIALS & CONTINUA
(2022)
Article
Computer Science, Information Systems
Sami Ullah, Muhammad Ramzan Talib, Toqir A. Rana, Muhammad Kashif Hanif, Muhammad Awais
Summary: This paper proposes a model that utilizes deep learning and machine learning approaches for the classification of users' emotions from Urdu conversational text. The experimental evaluation shows encouraging results with 67% accuracy for Urdu dialogue datasets.
CMC-COMPUTERS MATERIALS & CONTINUA
(2022)
Article
Automation & Control Systems
Toqir A. Rana, Bahrooz Bakht, Mehtab Afzal, Natash Ali Mian, Muhammad Waseem Iqbal, Abbas Khalid, Muhammad Raza Naqvi
Summary: The paper focuses on opinion target extraction in the Urdu language domain by crafting syntactic rules to identify users' opinions and associated target words. The proposed methodology achieves promising performance compared to existing English language approaches.
INTELLIGENT AUTOMATION AND SOFT COMPUTING
(2021)
Article
Computer Science, Information Systems
Ahmad Amin, Toqir A. Rana, Natash Ali Mian, Muhammad Waseem Iqbal, Abbas Khalid, Tahir Alyas, Mohammad Tubishat
Proceedings Paper
Computer Science, Artificial Intelligence
Kamal Karkonasasi, Cheah Yu-N, Seyed Aliakbar Mousavi, Ahmad Suhaimi Baharudin
EMERGING TRENDS IN INTELLIGENT COMPUTING AND INFORMATICS: DATA SCIENCE, INTELLIGENT INFORMATION SYSTEMS AND SMART COMPUTING
(2020)
Article
Computer Science, Information Systems
Mohammad Tubishat, Mohammed Alswaitti, Seyedali Mirjalili, Mohammed Ali Al-Garadi, Ma'en Tayseer Alrashdan, Toqir A. Rana
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)
