Article
Computer Science, Information Systems
Ibtissam Touahri, Azzeddine Mazroui
Summary: This paper proposes a new approach to analyze sentiments for the Arabic language by building new lexical resources and integrating morphological notions. The resources are used to construct a supervised model and semantically segment the lexicon to improve execution time.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Sarah Saad Eldin, Ammar Mohammed, Ahmed Sharaf Eldin, Hesham Hefny
Summary: With the increasing number of product reviews, customer sentiment analysis has become a cumbersome task. Feature-based opinion retrieval systems have emerged as an effective tool for analyzing and expressing customer sentiments towards services. The proposed enhanced retrieval approach in this study showed improved ranking results by extracting more features, both implicit and explicit, compared to other methods such as conditional random field and association rule mining.
JOURNAL OF INTELLIGENT INFORMATION SYSTEMS
(2021)
Article
Computer Science, Information Systems
Hiren Kumar Thakkar, Prasan Kumar Sahoo, Pranab Mohanty
Summary: This paper introduces a novel method for domain feature retrieval in text summarization, formulating the problem as a clustering problem and utilizing three newly conceived empirical observations. Two algorithms are designed to identify domain features, with experimental results demonstrating the robustness of the method in domain feature retrieval and summarization.
INFORMATION PROCESSING & MANAGEMENT
(2021)
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
Engineering, Multidisciplinary
Anima Pradhan, Manas Ranjan Senapati, Pradip Kumar Sahu
Summary: This study proposes a multi-level learning approach, including statistical methods, pattern-based methods, and rule-based methods, for aspect extraction and sentiment polarity identification from written texts. By applying probabilistic graphical models and lexicon retrieval, latent topic terms and associated opinion words are generated. To address polarity shift, a hybrid approach of rule-based methods and a graph-theoretic model is used. Experimental results on restaurant and laptop datasets show that the proposed method outperforms baseline methods in aspect extraction and sentiment classification.
AIN SHAMS ENGINEERING JOURNAL
(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)
Article
Computer Science, Artificial Intelligence
Omid Mohamad Beigi, Mohammad H. Moattar
Summary: This paper proposes a novel approach to sentiment analysis using a hybrid of neural networks and sentiment lexicon, effectively addressing inaccuracies and labeling issues in the field. The approach outperforms several alternative previous approaches of unsupervised domain adaptation.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Tinghuai Ma, Huan Rong, Yongsheng Hao, Jie Cao, Yuan Tian, Mznah Al-Rodhaan
Summary: This research focuses on sentiment polarity detection from online user-generated text. The existing lexicon-based methods suffer from polarity fuzziness, where the same word can have opposite polarities in different seed lexicons. To address this issue, the study proposes a two-aspect lexicon expansion approach to enhance Chinese sentiment polarity detection. By detecting and revising sentiment polarity for new and existing words in seed lexicons and incorporating fine-grained sentiment processing through symmetrical mapping, sentiment feature pruning, and text representation, the proposed framework achieves the best overall performance compared to other methods.
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
(2022)
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
Mathematics
Yuncong Li, Fang Wang, Sheng-hua Zhong
Summary: Sentiment analysis studies affective states and subjective information in digital text using computational methods. Aspect Sentiment Triplet Extraction (ASTE) aims to extract aspect term, sentiment, and opinion term triplets from sentences. However, some ASTE's extracted triplets only reflect the sentence's sentiment towards the aspect term, not the sentiment between the aspect and opinion terms. This paper introduces a more nuanced task, Aspect-Sentiment-Opinion Triplet Extraction (ASOTE), which extracts triplets where the sentiment is based on the aspect term and opinion term pair. A Position-aware BERT-based Framework (PBF) is proposed to address ASOTE, achieving benchmark performance on four datasets.
Article
Computer Science, Information Systems
Nurul Husna Mahadzir, Mohd Faizal Omar, Mohd Nasrun Mohd Nawi, Anas A. Salameh, Kasmaruddin Che Hussin, Abid Sohail
Summary: This paper addresses the lack of sentiment lexicon in sentiment analysis research in the Malaysian context and proposes a new bilingual sentiment lexicon called MELex. The approach differs from previous works as MELex can analyze text for both Malay and English languages with 90% accuracy. It is evaluated based on experimentation and case study on affordable housing projects in Malaysia, showing implications for analyzing public sentiments in the Malaysian context. The paper introduces a new technique for assigning polarity score and improves the performance of classifying mixed language content.
CMC-COMPUTERS MATERIALS & CONTINUA
(2022)
Article
Computer Science, Artificial Intelligence
Manju Venugopalan, Deepa Gupta
Summary: Aspect level sentiment analysis is a fine-grained task that extracts aspects and their sentiment polarity from opinionated text. This research proposes an unsupervised model that uses minimal aspect seed words to guide the extraction process and enhance the performance. The model incorporates guided inputs, multiple pruning strategies, and semantic filters to improve performance. Evaluation results show competitive and appreciable performance on restaurant domain datasets.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Health Care Sciences & Services
Chaixiu Li, Jiaqi Fu, Jie Lai, Lijun Sun, Chunlan Zhou, Wenji Li, Biao Jian, Shisi Deng, Yujie Zhang, Zihan Guo, Yusheng Liu, Yanni Zhou, Shihui Xie, Mingyue Hou, Ru Wang, Qinjie Chen, Yanni Wu
Summary: This study constructed an emotional lexicon for patients with breast cancer, consisting of 9357 words covering 8 fine-grained emotional categories. The lexicon outperformed existing emotional lexicons C-LIWC and HowNet, providing a new tool and method for emotion recognition and management in patients with breast cancer.
JOURNAL OF MEDICAL INTERNET RESEARCH
(2023)
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
Engineering, Electrical & Electronic
Yu Wang, Fuji Ren, Changqin Quan
IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING
(2018)
Article
Engineering, Electrical & Electronic
Ying Zhao, Zhiwei Luo, Changqin Quan
EURASIP JOURNAL ON IMAGE AND VIDEO PROCESSING
(2018)
Article
Chemistry, Multidisciplinary
Wenjun Bai, Changqin Quan, Zhiwei Luo
APPLIED SCIENCES-BASEL
(2018)
Article
Chemistry, Multidisciplinary
Wenjun Bai, Changqin Quan, Zhi-Wei Luo
APPLIED SCIENCES-BASEL
(2019)
Article
Computer Science, Artificial Intelligence
Ying Zhao, Zhiwei Luo, Changqin Quan, Dianchao Liu, Gang Wang
PATTERN RECOGNITION
(2020)
Article
Chemistry, Multidisciplinary
Changqin Quan, Zhiwei Luo, Song Wang
APPLIED SCIENCES-BASEL
(2020)
Article
Chemistry, Multidisciplinary
Tomohiro Fujita, Zhiwei Luo, Changqin Quan, Kohei Mori, Sheng Cao
Summary: This paper investigates a novel RNN model with sech gate and RP images for PD detection task, which shows competitive performance in accuracy and learning speed compared to traditional models. Future work will focus on further improving detection performance by analyzing input sound types, RP image sizes, and deep learning structures.
APPLIED SCIENCES-BASEL
(2021)
Article
Computer Science, Artificial Intelligence
Wenjun Bai, Changqin Quan, Zhi-Wei Luo
Summary: In this research, a novel generative model, encapsulated variational auto-encoders (EVAE), is proposed to generate facial expressions along the psychological conceptualised Arousal-Valence dimensions. The model's feasibility is demonstrated through empirical validations on publicly available facial expression datasets, and the importance of the data-driven Arousal-Valence plane in affective computing is highlighted.
COGNITIVE COMPUTATION
(2023)
Article
Engineering, Biomedical
Changqin Quan, Kang Ren, Zhiwei Luo, Zhonglue Chen, Yun Ling
Summary: This paper proposes a novel deep learning model for Parkinson's disease detection from speech signals. The model extracts time series dynamic features using 2D-CNNs and captures the dependencies between them using 1D-CNN. The proposed model outperformed other machine learning models and achieved high accuracies on speech tasks in different languages. The features generated by the model were able to capture the characteristics of Parkinson's disease sounds, such as reduced overall frequency range and variability. The low-frequency region of the Mel-spectrogram was found to be more influential and important for Parkinson's disease detection from speech.
BIOCYBERNETICS AND BIOMEDICAL ENGINEERING
(2022)
Article
Computer Science, Information Systems
Changqin Quan, Kang Ren, Zhiwei Luo
Summary: This study examines the detection of Parkinson's Disease through static and dynamic speech features, with promising results showing that utilizing a Bidirectional LSTM model can significantly improve the accuracy of PD detection.
Proceedings Paper
Computer Science, Artificial Intelligence
Ying Zhao, Zhiwei Luo, Changqin Quan, Dianchao Liu, Gang Wang
MULTIMEDIA MODELING (MMM 2020), PT II
(2020)
Proceedings Paper
Computer Science, Artificial Intelligence
Wenjun Bai, Changqin Quan, Zhi-Wei Luo
COMPUTER AND INFORMATION SCIENCE (ICIS 2018)
(2019)
Proceedings Paper
Computer Science, Artificial Intelligence
Tomohiro Fujita, Wenjun Bai, Changqin Quan
2017 18TH IEEE/ACIS INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING, ARTIFICIAL INTELLIGENCE, NETWORKING AND PARALLEL/DISTRIBUTED COMPUTING (SNDP 2017)
(2017)
Proceedings Paper
Computer Science, Artificial Intelligence
Ying Zhao, Zhiwei Luo, Changqin Quan
2017 14TH CONFERENCE ON COMPUTER AND ROBOT VISION (CRV 2017)
(2017)
Article
Computer Science, Software Engineering
Xiao Sun, Chongyuan Sun, Changqin Quan, Fuji Ren, Fang Tian, Kunxia Wang
INTERNATIONAL JOURNAL OF NETWORKED AND DISTRIBUTED COMPUTING
(2017)
Article
Computer Science, Information Systems
Xia Liang, Jie Guo, Peide Liu
Summary: This paper investigates a novel consensus model based on social networks to manage manipulative and overconfident behaviors in large-scale group decision-making. By proposing a novel clustering model and improved methods, the consensus reaching is effectively facilitated. The feedback mechanism and management approach are employed to handle decision makers' behaviors. Simulation experiments and comparative analysis demonstrate the effectiveness of the model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiang Li, Haiwang Guo, Xinyang Deng, Wen Jiang
Summary: This paper proposes a method based on class gradient networks for generating high-quality adversarial samples. By introducing a high-level class gradient matrix and combining classification loss and perturbation loss, the method demonstrates superiority in the transferability of adversarial samples on targeted attacks.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Lingyun Lu, Bang Wang, Zizhuo Zhang, Shenghao Liu
Summary: Many recommendation algorithms only rely on implicit feedbacks due to privacy concerns. However, the encoding of interaction types is often ignored. This paper proposes a relation-aware neural model that classifies implicit feedbacks by encoding edges, thereby enhancing recommendation performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jaehong Yu, Hyungrok Do
Summary: This study discusses unsupervised anomaly detection using one-class classification, which determines whether a new instance belongs to the target class by constructing a decision boundary. The proposed method uses a proximity-based density description and a regularized reconstruction algorithm to overcome the limitations of existing one-class classification methods. Experimental results demonstrate the superior performance of the proposed algorithm.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Hui Tu, Shifei Ding, Xiao Xu, Haiwei Hou, Chao Li, Ling Ding
Summary: Border-Peeling algorithm is a density-based clustering algorithm, but its complexity and issues on unbalanced datasets restrict its application. This paper proposes a non-iterative border-peeling clustering algorithm, which improves the clustering performance by distinguishing and associating core points and border points.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Long Tang, Pan Zhao, Zhigeng Pan, Xingxing Duan, Panos M. Pardalos
Summary: In this work, a two-stage denoising framework (TSDF) is proposed for zero-shot learning (ZSL) to address the issue of noisy labels. The framework includes a tailored loss function to remove suspected noisy-label instances and a ramp-style loss function to reduce the negative impact of remaining noisy labels. In addition, a dynamic screening strategy (DSS) is developed to efficiently handle the nonconvexity of the ramp-style loss.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Raghunathan Krishankumar, Sundararajan Dhruva, Kattur S. Ravichandran, Samarjit Kar
Summary: Health 4.0 is gaining global attention for better healthcare through digital technologies. This study proposes a new decision-making framework for selecting viable blockchain service providers in the Internet of Medical Things (IoMT). The framework addresses the limitations in previous studies and demonstrates its applicability in the Indian healthcare sector. The results show the top ranking BSPs, the importance of various criteria, and the effectiveness of the developed model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Tao Tan, Hong Xie, Liang Feng
Summary: This paper proposes a heterogeneous update idea and designs HetUp Q-learning algorithm to enlarge the normalized gap by overestimating the Q-value corresponding to the optimal action and underestimating the Q-value corresponding to the other actions. To address the limitation, a softmax strategy is applied to estimate the optimal action, resulting in HetUpSoft Q-learning and HetUpSoft DQN. Extensive experimental results show significant improvements over SOTA baselines.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Chao Yang, Xianzhi Wang, Lina Yao, Guodong Long, Guandong Xu
Summary: This paper proposes a dynamic transformer-based architecture called Dyformer for multivariate time series classification. Dyformer captures multi-scale features through hierarchical pooling and adaptive learning strategies, and improves model performance by introducing feature-map-wise attention mechanisms and a joint loss function.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiguang Li, Baolu Feng, Yunhe Sun, Ammar Hawbani, Saeed Hammod Alsamhi, Liang Zhao
Summary: This paper proposes an enhanced scatter search strategy, using opposition-based learning, to solve the problem of automated test case generation based on path coverage (ATCG-PC). The proposed ESSENT algorithm selects the path with the lowest path entropy among the uncovered paths as the target path and generates new test cases to cover the target path by modifying the dimensions of existing test cases. Experimental results show that the ESSENT algorithm outperforms other state-of-the-art algorithms, achieving maximum path coverage with fewer test cases.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Shirin Dabbaghi Varnosfaderani, Piotr Kasprzak, Aytaj Badirova, Ralph Krimmel, Christof Pohl, Ramin Yahyapour
Summary: Linking digital accounts belonging to the same user is crucial for security, user satisfaction, and next-generation service development. However, research on account linkage is mainly focused on social networks, and there is a lack of studies in other domains. To address this, we propose SmartSSO, a framework that automates the account linkage process by analyzing user routines and behavior during login processes. Our experiments on a large dataset show that SmartSSO achieves over 98% accuracy in hit-precision.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Renchao Wu, Jianjun He, Xin Li, Zuguo Chen
Summary: This paper proposes a memetic algorithm with fuzzy-based population control (MA-FPC) to solve the joint order batching and picker routing problem (JOBPRP). The algorithm incorporates batch exchange crossover and a two-level local improvement procedure. Experimental results show that MA-FPC outperforms existing algorithms in terms of solution quality.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Guoxiang Zhong, Fagui Liu, Jun Jiang, Bin Wang, C. L. Philip Chen
Summary: In this study, we propose the AMFormer framework to address the problem of mixed normal and anomaly samples in deep unsupervised time-series anomaly detection. By refining the one-class representation and introducing the masked operation mechanism and cost sensitive learning theory, our approach significantly improves anomaly detection performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jin Zhou, Kang Zhou, Gexiang Zhang, Ferrante Neri, Wangyang Shen, Weiping Jin
Summary: In this paper, the authors focus on the issue of multi-objective optimisation problems with redundant variables and indefinite objective functions (MOPRVIF) in practical problem-solving. They propose a dual data-driven method for solving this problem, which consists of eliminating redundant variables, constructing objective functions, selecting evolution operators, and using a multi-objective evolutionary algorithm. The experiments conducted on two different problem domains demonstrate the effectiveness, practicality, and scalability of the proposed method.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Georgios Charizanos, Haydar Demirhan, Duygu Icen
Summary: This article proposes a new fuzzy logistic regression framework that addresses the problems of separation and imbalance while maintaining the interpretability of classical logistic regression. By fuzzifying binary variables and classifying subjects based on a fuzzy threshold, the framework demonstrates superior performance on imbalanced datasets.
INFORMATION SCIENCES
(2024)
