Article
Environmental Sciences
Meilan Chen, Zhiying Guo, Kashif Abbass, Wenfeng Huang
Summary: This paper explores the relationship between investor sentiment and stock excess return using an unsupervised learning approach, and finds that sentiment classified by theme is positively correlated with excess return. The degree of influence varies among different themes, and there is an asymmetric effect of sentiment on excess return.
FRONTIERS IN ENVIRONMENTAL SCIENCE
(2022)
Article
Computer Science, Information Systems
Belgacem Brahimi, Mohamed Touahria, Abdelkamel Tari
Summary: This paper proposes methods to extract valuable opinions from online movie reviews using n-gram and skip-n-gram models, subjective words, and feature reduction techniques to enhance sentiment analysis in Arabic. Experimental results demonstrate the effectiveness of these methods in improving sentiment classification results.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Sarah Omar Alhumoud, Asma Ali Al Wazrah
Summary: The amount of Arabic content created on websites and social media has significantly increased in the past decade, leading to a rise in studies using recurrent neural networks (RNNs) for Arabic sentiment analysis. These studies vary in the areas they address, the functionality and weaknesses of the models, and the number and scale of available datasets.
ARTIFICIAL INTELLIGENCE REVIEW
(2022)
Article
Computer Science, Information Systems
Tao Zhou, Kris Law, Douglas Creighton
Summary: This study proposes a novel joint sentiment-topic model that integrates a graph convolutional network and importance sampling-based training method, enabling efficient identification of sentiment for multiple topics and improving topic modeling and multi-topic identification.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Amjad Osmani, Jamshid Bagherzadeh Mohasefi
Summary: Sentiment analysis has significant impact in various fields, and topic modeling is an intriguing concept in emotion detection. This study proposes two novel topic models that extend and improve the Joint Sentiment-Topic model by considering the influence of the author's view on words in a text document. The proposed methods outperform baseline models in terms of accuracy and perplexity.
INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING
(2023)
Article
Education & Educational Research
Bingxin Du
Summary: This study analyzed the factors influencing learner satisfaction and found that course schedule, workload and completion status, video, instructor, content, and evaluation topics have a significant impact on learner satisfaction. However, perceived difficulty, structure, and interaction are not related to learner satisfaction.
EDUCATION AND INFORMATION TECHNOLOGIES
(2023)
Article
Chemistry, Multidisciplinary
Akhmedov Farkhod, Akmalbek Abdusalomov, Fazliddin Makhmudov, Young Im Cho
Summary: This study employs unsupervised machine learning to discover sentiment polarity at both document and word levels.
Utilizing the TDS model based on JST and LDA techniques, the analysis achieved good results in sentiment analysis accuracy.
The experimental results demonstrated high accuracy in document- and word-level sentiment classifications.
APPLIED SCIENCES-BASEL
(2021)
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
Computer Science, Artificial Intelligence
Xun Wang, Ting Zhou, Xiaoyang Wang, Yili Fang
Summary: Sentiment mining aims to understand market feedback by analyzing user comments, but bias in comments can distort results. A proposed framework takes harshness into account to improve sentiment analysis accuracy, surpassing existing methods in experimental evaluations.
EXPERT SYSTEMS WITH APPLICATIONS
(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
Arif Ullah, Sundas Naqeeb Khan, Nazri Mohd Nawi
Summary: The progression of social media popularity has generated a significant amount of textual data, allowing reviewers to share their comments and opinions about various subjects. This data is crucial for businesses and industries as it influences demand and supply. With the increase in textual data, sentiment analysis and opinion mining have become popular research areas.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Psychology, Multidisciplinary
Chuang Li, Yating Niu, Liping Wang
Summary: This paper conducts sentiment analysis and text mining on 85306 online reviews of 8 typical green products in China's JD.com e-commerce platform, aiming to enhance the public's awareness of green consumption. The study findings reveal that consumers' preferences and evaluations of different green products vary, but overall, the public has a high satisfaction with existing products. The paper also identifies various positive and negative sentiments related to green products and highlights the upward trend of consumer attention towards most attributes of these products. The results provide reliable evidence for promoting and utilizing green products, which is of great significance for encouraging green consumption.
COMPUTERS IN HUMAN BEHAVIOR
(2023)
Article
Computer Science, Information Systems
Chen Yang, Xiaohong Chen, Lei Liu, Penny Sweetser
Summary: Personalized recommendation systems can extract user preferences by analyzing sentiment polarity in user review content, and generate recommendation results through a novel voting mechanism, outperforming traditional algorithms in experiments.
INFORMATION PROCESSING & MANAGEMENT
(2021)
Review
Computer Science, Theory & Methods
Shervin Minaee, Nal Kalchbrenner, Erik Cambria, Narjes Nikzad, Meysam Chenaghlu, Jianfeng Gao
Summary: This article provides a comprehensive review of over 150 deep learning-based models for text classification developed in recent years. It discusses their technical contributions, similarities, and strengths, as well as summarizes popular datasets used for text classification. The article also includes a quantitative analysis of the performance of different deep learning models on popular benchmarks and discusses future research directions.
ACM COMPUTING SURVEYS
(2022)
Article
Education & Educational Research
Anna Koufakou
Summary: This paper discusses how to analyze student opinions and topics using machine learning techniques. By comparing different methods, the researchers found that RoBERTa and SVM performed the best in sentiment polarity and topic classification. These findings are valuable for educational institutions and course providers to conduct self-evaluation and improvement.
EDUCATION AND INFORMATION TECHNOLOGIES
(2023)
Article
Computer Science, Information Systems
Shufeng Xiong, Donghong Ji
INFORMATION PROCESSING & MANAGEMENT
(2016)
Article
Computer Science, Information Systems
Shufeng Xiong, Donghong Ji
INFORMATION SCIENCES
(2016)
Article
Computer Science, Artificial Intelligence
Shufeng Xiong, Hailian Lv, Weiting Zhao, Donghong Ji
Review
Chemistry, Multidisciplinary
Bingkun Wang, Shufeng Xiong, Yongfeng Huang, Xing Li
APPLIED SCIENCES-BASEL
(2018)
Article
Computer Science, Artificial Intelligence
Shufeng Xiong, Ming Cheng, Vishwash Batra, Tao Qian, Bingkun Wang, Yangdong Ye
INFORMATION FUSION
(2020)
Article
Computer Science, Information Systems
Shufeng Xiong, Li Ma, Ming Cheng, Bingkun Wang
Summary: In this paper, a neural self-attention model is proposed for Pinyin Sequence to Chinese Sequence conversion, which outperforms other methods on a medical domain dataset and achieves comparable performance on a general dataset.
MULTIMEDIA SYSTEMS
(2022)
Article
Mathematical & Computational Biology
Shufeng Xiong, Vishwash Batra, Liangliang Liu, Lei Xi, Changxia Sun
Summary: The study implemented a domain attention mechanism for detecting personal medication intake, showing promising results in experiments and outperforming multiple baseline models.
COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE
(2022)
Article
Physics, Multidisciplinary
Shufeng Xiong, Xiaobo Fan, Vishwash Batra, Yiming Zeng, Guipei Zhang, Lei Xi, Hebing Liu, Lei Shi
Summary: This paper presents a new benchmark dataset for Chinese textual affective structure (CTAS) to advance research in affective understanding in artificial intelligence. The dataset is based on Weibo, the most popular Chinese social media platform, and includes comprehensive affective structure labels. The proposed maximum entropy Markov model, incorporating neural network features, outperforms the two baseline models.
Article
Computer Science, Artificial Intelligence
Shufeng Xiong, Guipei Zhang, Vishwash Batra, Lei Xi, Lei Shi, Liangliang Liu
Summary: Compared to ordinary news, fake news spreads faster with lower production cost, causing significant social harm. Detecting fake news efficiently and accurately has become a research focus due to these reasons. We propose a Two-Round Inconsistency-based Multi-modal fusion Network (TRIMOON) for fake news detection, consisting of feature extraction, fusion, and classification modules. By performing two-fold inconsistency detection, we effectively filter noise generated during the fusion process. Experimental results demonstrate the superiority of our TRIMOON model over state-of-the-art approaches on Chinese and English datasets.
INFORMATION FUSION
(2023)
Article
Computer Science, Information Systems
Liangliang Liu, Jing Chang, Pei Zhang, Hongbo Qiao, Shufeng Xiong
Summary: This paragraph discusses feature vectors, graph convolutional networks, low-grade glioma, self-attention, and similarity coefficients.
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
(2023)
Article
Computer Science, Information Systems
Liangliang Liu, Jing Chang, Gongbo Liang, Shufeng Xiong
Summary: In this study, a simulated quantum mechanics-based joint learning network (SQMLP-net) is proposed for simultaneous stroke lesion segmentation and TICI grade assessment. The network addresses the correlation and heterogeneity issues between the two tasks using a single-input double-output hybrid network. SQMLP-net consists of a segmentation branch and a classification branch, which share an encoder to extract and share spatial and global semantic information. Both tasks are optimized with a novel joint loss function that learns the intra- and inter-task weights. Evaluation on a public stroke dataset (ATLAS R2.0) shows that SQMLP-net achieves state-of-the-art metrics (Dice: 70.98% and accuracy: 86.78%) and outperforms single-task and existing advanced methods. An analysis reveals a negative correlation between the severity of TICI grading and the accuracy of stroke lesion segmentation.
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
(2023)
Article
Multidisciplinary Sciences
Shufeng Xiong, Wenjie Tian, Vishwash Batra, Xiaobo Fan, Lei Xi, Hebing Liu, Liangliang Liu
Summary: Given the importance of regulatory authorities and the growing demand for information disclosure, a large amount of food safety news reports can be found on the Internet. Extracting and classifying this information accurately, as well as providing appropriate safety alerts, has become a challenging problem for academic research. This paper proposes a long-text classification model using hierarchical Transformers, which outperforms existing methods and establishes itself as the leading approach in terms of performance.
Article
Biochemical Research Methods
Yinxia Lou, Yue Zhang, Tao Qian, Fei Li, Shufeng Xiong, Donghong Ji
Article
Computer Science, Information Systems
Bingkun Wang, Weina He, Zhen Yang, Shufeng Xiong
Article
Computer Science, Artificial Intelligence
Rui Lv, Dingheng Wang, Jiangbin Zheng, Zhao-Xu Yang
Summary: In this paper, the authors investigate tensor decomposition for neural network compression. They analyze the convergence and precision of tensor mapping theory, validate the rationality of tensor mapping and its superiority over traditional tensor approximation based on the Lottery Ticket Hypothesis. They propose an efficient method called 3D-KCPNet to compress 3D convolutional neural networks using the Kronecker canonical polyadic (KCP) tensor decomposition. Experimental results show that 3D-KCPNet achieves higher accuracy compared to the original baseline model and the corresponding tensor approximation model.
Article
Computer Science, Artificial Intelligence
Xiangkun He, Zhongxu Hu, Haohan Yang, Chen Lv
Summary: In this paper, a novel constrained multi-objective reinforcement learning algorithm is proposed for personalized end-to-end robotic control with continuous actions. The approach trains a single model using constraint design and a comprehensive index to achieve optimal policies based on user-specified preferences.
Article
Computer Science, Artificial Intelligence
Zhijian Zhuo, Bilian Chen, Shenbao Yu, Langcai Cao
Summary: In this paper, a novel method called Expansion with Contraction Method for Overlapping Community Detection (ECOCD) is proposed, which utilizes non-negative matrix factorization to obtain disjoint communities and applies expansion and contraction processes to adjust the degree of overlap. ECOCD is applicable to various networks with different properties and achieves high-quality overlapping community detection.
Article
Computer Science, Artificial Intelligence
Yizhe Zhu, Chunhui Zhang, Jialin Gao, Xin Sun, Zihan Rui, Xi Zhou
Summary: In this work, the authors propose a Contrastive Spatio-Temporal Distilling (CSTD) approach to improve the detection of high-compressed deepfake videos. The approach leverages spatial-frequency cues and temporal-contrastive alignment to fully exploit spatiotemporal inconsistency information.
Review
Computer Science, Artificial Intelligence
Laijin Meng, Xinghao Jiang, Tanfeng Sun
Summary: This paper provides a review of coverless steganographic algorithms, including the development process, known contributions, and general issues in image and video algorithms. It also discusses the security of coverless steganography from theoretical analysis to actual investigation for the first time.
Article
Computer Science, Artificial Intelligence
Yajie Bao, Tianwei Xing, Xun Chen
Summary: Visual question answering requires processing multi-modal information and effective reasoning. Neural-symbolic learning is a promising method, but current approaches lack uncertainty handling and can only provide a single answer. To address this, we propose a confidence based neural-symbolic approach that evaluates NN inferences and conducts reasoning based on confidence.
Article
Computer Science, Artificial Intelligence
Anh H. Vo, Bao T. Nguyen
Summary: Interior style classification is an interesting problem with potential applications in both commercial and academic domains. This project proposes a method named ISC-DeIT, which combines data-efficient image transformer architectures and knowledge distillation, to address the interior style classification problem. Experimental results demonstrate a significant improvement in predictive accuracy compared to other state-of-the-art methods.
Article
Computer Science, Artificial Intelligence
Shashank Kotyan, Danilo Vasconcellos Vargas
Summary: This article introduces a novel augmentation technique called Dynamic Scanning Augmentation to improve the accuracy and robustness of Vision Transformer (ViT). The technique leverages dynamic input sequences to adaptively focus on different patches, resulting in significant changes in ViT's attention mechanism. Experimental results demonstrate that Dynamic Scanning Augmentation outperforms ViT in terms of both robustness to adversarial attacks and accuracy against natural images.
Article
Computer Science, Artificial Intelligence
Hiba Alqasir, Damien Muselet, Christophe Ducottet
Summary: The article proposes a solution to improve the learning process of a classification network by providing shape priors, reducing the need for annotated data. The solution is tested on cross-domain digit classification tasks and a video surveillance application.
Article
Computer Science, Artificial Intelligence
Dexiu Ma, Mei Liu, Mingsheng Shang
Summary: This paper proposes a method using neural dynamics solvers to solve infinity-norm optimization problems. Two improved solvers are constructed and their effectiveness and superiority are demonstrated through theoretical analysis and simulation experiments.
Article
Computer Science, Artificial Intelligence
Francesco Gregoretti, Giovanni Pezzulo, Domenico Maisto
Summary: Active Inference is a computational framework that uses probabilistic inference and variational free energy minimization to describe perception, planning, and action. cpp-AIF is a header-only C++ library that provides a powerful tool for implementing Active Inference for Partially Observable Markov Decision Processes through multi-core computing. It is cross-platform and improves performance, memory management, and usability compared to existing software.
Article
Computer Science, Artificial Intelligence
Zelin Ying, Dawei Cheng, Cen Chen, Xiang Li, Peng Zhu, Yifeng Luo, Yuqi Liang
Summary: This paper proposes a novel stock market trends prediction framework called SMART, which includes a self-supervised stock technical data sequence embedding model S3E. By training with multiple self-supervised auxiliary tasks, the model encodes stock technical data sequences into embeddings and uses the learned sequence embeddings for predicting stock market trends. Extensive experiments on China A-Shares market and NASDAQ market prove the high effectiveness of our model in stock market trends prediction, and its effectiveness is further validated in real-world applications in a leading financial service provider in China.
Article
Computer Science, Artificial Intelligence
Hao Li, Hao Jiang, Dongsheng Ye, Qiang Wang, Liang Du, Yuanyuan Zeng, Liu Yuan, Yingxue Wang, C. Chen
Summary: DHGAT1, a dynamic hyperbolic graph attention network, utilizes hyperbolic metric properties to embed dynamic graphs. It employs a spatiotemporal self-attention mechanism and weighted node representations, resulting in excellent performance in link prediction tasks.
Article
Computer Science, Artificial Intelligence
Jiehui Huang, Zhenchao Tang, Xuedong He, Jun Zhou, Defeng Zhou, Calvin Yu-Chian Chen
Summary: This study proposes a progressive learning multi-scale feature blending model for image deraining tasks. The model utilizes detail dilation and texture extraction to improve the restoration of rainy images. Experimental results show that the model achieves near state-of-the-art performance in rain removal tasks and exhibits better rain removal realism.
Article
Computer Science, Artificial Intelligence
Lizhi Liu, Zilin Gao, Yinhe Wang, Yongfu Li
Summary: This paper proposes a novel discrete-time interconnected model for depicting complex dynamical networks. The model consists of nodes and edges subsystems, which consider the dynamic characteristic of both nodes and edges. By designing control strategies and coupling modes, the stabilization and synchronization of the network are achieved. Simulation results demonstrate the effectiveness of the proposed methods.
