Article
Chemistry, Multidisciplinary
Qizhi Li, Xianyong Li, Yajun Du, Yongquan Fan, Xiaoliang Chen
Summary: This paper proposes a new sentiment-enhanced word embedding method to improve sentence-level sentiment classification. By leveraging the mapping relationship between word embeddings and sentiment orientations, the method achieves higher accuracy and F1 values and reduces convergence time in sentiment classification models.
APPLIED SCIENCES-BASEL
(2022)
Article
Business
Chia-Hsuan Chang, San-Yih Hwang, Ming-Lun Wu
Summary: High-quality sentiment lexicons are crucial for lexicon-based sentiment analysis, but most lexicons are only available in certain dominant languages, limiting their applicability in specific domains or languages. This paper proposes a multistep approach for bilingual sentiment lexicon induction to disambiguate words with opposite sentiment polarities, which outperforms existing lexicons and competing approaches in terms of accuracy and coverage, using experiments on real-world online review data sets.
ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Rong Xiang, Jing Li, Mingyu Wan, Jinghang Gu, Qin Lu, Wenjie Li, Chu-Ren Huang
Summary: This study introduces a novel approach to incorporate external affective knowledge into neural networks for sentiment analysis, showing superior performance over traditional neural networks in all benchmark tests and with significant positive effects on model enhancement.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Rini Wijayanti, Andria Arisal
Summary: A novel Indonesian sentiment lexicon (SentIL) was created using an automatic pipeline, involving seed word creation, slang words and emoticons addition, and sentiment value tuning. Experimental results showed a significant increase in lexicon size and improved accuracy, outperforming other available Indonesian sentiment lexicons.
ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING
(2021)
Article
Computer Science, Artificial Intelligence
Zongxi Li, Haoran Xie, Gary Cheng, Qing Li
Summary: This paper proposes a novel method to generate a word-level emotion distribution vector, which can better understand the category and intensity of word-level emotion. Experimental results show that the proposed method produces competitive results compared to existing techniques.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Rezvan MohammadiBaghmolaei, Ali Ahmadi
Summary: This article investigates the application of word embedding models in emotion analysis by embedding mixed emotions features into existing word-vectors, leading to improved performance in sentiment analysis.
JOURNAL OF INTELLIGENT INFORMATION SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Felipe Bravo-Marquez, Arun Khanchandani, Bernhard Pfahringer
Summary: This study introduces a method to automatically induce continuously updated sentiment lexicons by training incremental word sentiment classifiers from Twitter streams. Experimental results show that the approach allows for successful tracking of the sentiment of words over time.
COGNITIVE COMPUTATION
(2022)
Article
Computer Science, Information Systems
Zongxi Li, Xinhong Chen, Haoran Xie, Qing Li, Xiaohui Tao, Gary Cheng
Summary: This study explores emotion construction by incorporating fine-grained emotions and domain knowledge, proposing a novel method EmoChannel to capture intensity variation of specific emotions and utilizing a self-attention module to extract dependency relationships among emotions, enhancing emotion classification performance. The proposed method demonstrates competitive performance against state-of-the-art baselines on multi-class and sentiment analysis datasets.
WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS
(2021)
Article
Automation & Control Systems
Hande Aka Uymaz, Senem Kumova Metin
Summary: Detection of emotions from text has become an important research area in recent years. By adding emotional information to word vectors, researchers have achieved better results in emotion detection tasks, overcoming limitations of traditional word representation models.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Zahra Rahimi, Mohammad Mehdi Homayounpour
Summary: Word embeddings as feature learning techniques for natural language processing have limitations in sentiment analysis, but the proposed unsupervised models that integrate word polarity and co-occurrence information show higher performance in document-level sentiment analysis tasks.
APPLIED INTELLIGENCE
(2021)
Article
Computer Science, Artificial Intelligence
Hande Aka Uymaz, Senem Kumova Metin
Summary: Text is commonly used for natural language processing studies, but accurately reflecting meaning and detecting emotion pose challenges. Word embeddings, such as Word2Vec and GloVe, are frequently used to represent textual data and extract semantic information. However, these models may not capture emotive data effectively, leading to unexpected results in sentiment and emotion detection.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Mathematics
Zihao Lu, Xiaohui Hu, Yun Xue
Summary: This paper proposes a dual-word embedding model considering syntactic information for cross-domain sentiment classification. Experimental results show that the model outperforms other baselines on two real-world datasets.
Article
Computer Science, Cybernetics
K. Nimala, R. Jebakumar
Summary: The advancement of social platform services has expedited the sharing of emotions, with a proposed system analyzing student feedback to evaluate teacher performance and student satisfaction. Using sentiment topic model and emotion lexicons, the system can detect sarcasm, categorize emotions, and predict student satisfaction towards teacher performance. The developed social emotion lexicon can further determine meaningful latent topics focusing on emotions.
BEHAVIOUR & INFORMATION TECHNOLOGY
(2021)
Article
Computer Science, Artificial Intelligence
Eva Zangerle, Chih-Ming Chen, Ming-Feng Tsai, Yi-Hsuan Yang
Summary: This study analyzes the connection between users' emotional states and their musical choices, finding that affective information has a significant impact on music recommendation. Different ranking strategies are proposed based on emotional information and latent features, with those incorporating affective information and leveraging hashtags outperforming others in capturing context-specific preferences.
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Jingyao Tang, Yun Xue, Ziwen Wang, Shaoyang Hu, Tao Gong, Yinong Chen, Haoliang Zhao, Luwei Xiao
Summary: A Bayesian estimation-based sentiment word embedding model has been proposed, which effectively extracts sentiment information of low-frequency words and integrates it into word embedding learning through a novel loss function. Experimental results demonstrate that BESWE outperforms many state-of-the-art methods in sentiment analysis tasks.
CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY
(2022)
Article
Radiology, Nuclear Medicine & Medical Imaging
Xiang Li, Yuchen Jiang, Minglei Li, Jiusi Zhang, Shen Yin, Hao Luo
Summary: This study combines the manual labeling process of doctors and introduces the correlation between single-modality and the tumor subcomponents into the segmentation network. The method improves the segmentation performance of brain tumors and can be applied in the clinical practice.
Article
Radiology, Nuclear Medicine & Medical Imaging
Minglei Li, Yuchen Jiang, Xiang Li, Shen Yin, Hao Luo
Summary: This study proposes an ensemble framework that combines three types of dedicatedly-designed convolutional neural networks (CNNs) and a multilayer perceptron (MLP) network to overcome the limitations of existing methods. Experimental results show that the proposed ensemble framework achieves superior performance under most evaluation metrics.
Article
Plant Sciences
Nuerbiye Aobulikasimu, Dan Zheng, Peipei Guan, Lixiao Xu, Bo Liu, Minglei Li, Xueshi Huang, Li Han
Summary: Isoflavonoids from Radix Astragali, specifically methylnissolin (ML) and methylnissolin-3-O-beta-D-glucoside (MLG), have anti-inflammatory and hepatoprotective effects by downregulating the expression of proinflammatory cytokines through the NF-kappa B signaling pathway.
Article
Urology & Nephrology
Yidi Chi, Minglei Li, Shuofan Chen, Weiping Zhang, Pei Liu
Summary: This article reports a case of botryoid WT in a pediatric patient with left-sided inferior vena cava, emphasizing the importance of preoperative imaging in preoperative planning.
UROLOGIA INTERNATIONALIS
(2023)
Article
Urology & Nephrology
Zhiqiang Mo, Minglei Li, Xianghui Xie, Ning Sun, Weiping Zhang, Jun Tian, Hongcheng Song
Summary: This study investigated the urodynamic changes before and after posterior urethral valve (PUV) ablation and found that bladder compliance improved and maximum detrusor pressure decreased after the surgery, although they did not reach normal levels.
Article
Engineering, Environmental
Qi Tan, Zimo Yang, Shichen Bu, Jiangbin Chen, Wenjuan Chen, Wei Geng, Qi Huang, Limin Duan, Mengfei Guo, Yali Wu, Jingjing Deng, E. Zhou, Minglei Li, Feng Wu, Yang Jin
Summary: In this study, TMPs-PEI-LPS nanoparticles were synthesized as a delivery system for tumor antigens. The experiments demonstrated that TMPs-PEI-LPS efficiently induced the uptake and maturity of dendritic cells and exhibited anti-tumor effects in a mouse model of lung cancer. These findings suggest that TMPs-PEI-LPS is a promising platform for enhancing immunogenicity of tumor antigens.
CHEMICAL ENGINEERING JOURNAL
(2023)
Article
Electrochemistry
Ru Wang, Minglei Li, Yue Liu, Gaole Dai, Tingting Wu, Wei He, Shunan Feng, Xiaohong Zhang, Yu Zhao
Summary: In this study, a new strategy is proposed to tune the redox potentials of bipolar redox-active organic molecules (ROMs) with fused conjugation by incorporating electron-withdrawing or electron-donating groups. Three designed bipolar ROMs with fused conjugation based on phenoxazine derivatives exhibit highly reversible redox reactions in the given electrolyte systems. The symmetric organic redox flow batteries (ORFBs) based on these bipolar ROMs maintain decent electrochemical stability when cycled in a compatible electrolyte.
ELECTROCHEMISTRY COMMUNICATIONS
(2023)
Article
Engineering, Electrical & Electronic
Wenpeng Zong, Minglei Li, Guangyun Li, Li Wang, Long Wang, Fen Zhang
Summary: An efficient method based on plane segmentation and projection is proposed to extract as complete boundary line segments as possible. 3D planar point clouds generated by plane segmentation are converted into 2D images by graphical projection to detect boundary points. The projection resolution is determined through accurate statistics to preserve boundary details. Experimental results demonstrate its efficiency and automatic processing ability for large-scale point clouds.
IEEE SENSORS JOURNAL
(2023)
Article
Automation & Control Systems
Xing Xu, Xinwei Jiang, Ju Xie, Feng Wang, Minglei Li
Summary: This research focuses on planning a trajectory that reflects human driving behavior based on a test track, in order to improve the acceptability of autonomous vehicles in the market. The study includes data processing, trajectory analysis and planning, and verification experiments. Results show that the human driving characterised trajectory allows for smoother and more comfortable autonomous driving, and to a large extent reflects the characteristics of human drivers.
INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS
(2023)
Article
Medicine, Research & Experimental
Wenyun Huang, Wensi Niu, Hongmei Chen, Wujun Jiang, Yanbing Fu, Xiuxiu Li, Minglei Li, Jun Hua, Chunxia Hu
Summary: We aimed to develop a nomogram to predict the risk of severe influenza in previously healthy children. A total of 1135 children infected with influenza in a retrospective cohort study were included. Risk factors were identified through logistic regression analysis and a nomogram was established. The nomogram showed good predictive ability in both the training and validation cohorts.
JOURNAL OF INTERNATIONAL MEDICAL RESEARCH
(2023)
Article
Chemistry, Medicinal
Minglei Li, Ying Zhi, Bo Liu, Qingqiang Yao
Summary: Proteolysis-targeting chimeras (PROTACs) have emerged as a promising approach for degrading disease-causing proteins, especially those associated with tumors. The introduction of PROTACs has revolutionized antitumor drug development, leading to significant advancements in recent years. This review highlights the design strategy, development workflow, and future prospects of PROTAC technology, as well as addresses potential opportunities and challenges in PROTAC research.
JOURNAL OF MEDICINAL CHEMISTRY
(2023)
Article
Engineering, Multidisciplinary
MingLei Li, Yanfeng Geng, Guangliang Pan, Weiliang Wang, Hongyu Wang
Summary: In this paper, a network structure-cascaded dilated convolution vision informer (CDC-Vii) is proposed to accurately forecast the remaining useful life (RUL) of bearings using time-frequency fault features as input. CDC-Vii breaks the limitation of the original Informer, which is only sensitive to time-series information. Furthermore, a novel adaptive fault frequency band selection algorithm is introduced to reduce training time and utilize rich time-frequency information. Experimental results on two extensively utilized bearing datasets demonstrate that CDC-Vii outperforms advanced RUL prediction models.
MEASUREMENT SCIENCE AND TECHNOLOGY
(2023)
Article
Remote Sensing
Minglei Li, Qin Liu, Shu Peng
Summary: This letter presents a point cloud coarse registration method based on concave hull association. The proposed method extracts concave hulls and calculates unique feature descriptors for each vertex of the hulls. It achieves feature matching and finds the coarse registration parameters using the sample consensus initial alignment algorithm. The method has been verified on different datasets and compared with state-of-the-art algorithms, showing high efficiency and robustness.
REMOTE SENSING LETTERS
(2023)
Article
Medicine, General & Internal
Yingkai Ma, Yong Qin, Chen Liang, Xiang Li, Minglei Li, Ren Wang, Jinping Yu, Xiangning Xu, Songcen Lv, Hao Luo, Yuchen Jiang
Summary: The objective of this study is to develop a novel automatic convolutional neural network (CNN) for the diagnosis of meniscus injury and visualization of lesion characteristics, improving accuracy and reducing diagnosis times. A total of 1396 MRI images were used to train and test the cascaded-progressive convolutional neural network (C-PCNN). Results showed accuracy rates of 85.6% for anterior horn injury and 92% for posterior horn injury, with an average accuracy of 89.8% and AUC of 0.86. The C-PCNN assistant achieved diagnostic accuracy comparable to the chief physician.
Article
Chemistry, Physical
Minglei Li, Ru Wang, Tingting Wu, Yue Liu, Yuanyuan Chen, Wei He, Shunan Feng, Xiaohong Zhang, Gaole Dai, Yu Zhao
Summary: Organic materials are promising electrode materials for lithium-ion batteries due to their high theoretical capacity, abundant source, low cost, structure diversity, and environmental friendliness. By extending the conjugated structure and constructing an active unit based on 5,12-dihydrobenzo[b]phenazine(BPZ) and the corresponding polymer p-DPBPZ, the discharge voltage and stability of the organic electrode material is further enhanced. The study also demonstrates the potential of p-DPBPZ as an active material for sodium-ion batteries.
ACS APPLIED ENERGY MATERIALS
(2023)