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学者姓名：陈可嘉
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Abstract ：
In the aspect-based sentiment analysis task, the syntactic dependency parsing of the text is required first, which is highly dependent on the quality of the dependency parsing and does not take into account the lack of correlation between dependency parsing and semantic knowledge. Therefore, a two-channel graph convolution model based on syntactic perception and knowledge enhancement is proposed for the aspect-based sentiment analysis task. Syntax-perception mechanisms are used to learn sentence dependencies in one channel, and knowledge enhancement is performed in the other channel through a knowledge graph, with the Outputs of the two Channels correlated through an Information interaction mechanism, which allows the model to pay more syntactic and semantic attention to important words associated with aspectual words. In addition, a positional attention mechanism is introduced to adjust the score weights of words with respect to the position, which in turn improves the Performance of the aspect-based sentiment analysis task. Experiments are conducted on three public datasets, Restl4, Lapl4 and Twitter. Compared to other aspect-based sentiment analysis models, this paper' s model shows a more significant improvement in both accuracy and Fl value. Experiments show that syntactic perception and knowledge enhancement can guide the graph convolutional model to perform deeper semantic learning and reasonable weight allocation, thus improving the Performance of aspect-based sentiment analysis tasks. © 2024 Science Press. All rights reserved.
Keyword ：
attention mechanisms attention mechanisms dependency relations dependency relations graph convolutional network graph convolutional network knowledge graph knowledge graph sentiment analysis sentiment analysis
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| GB/T 7714 | Chen, K. , Zhang, Y. , Lin, H. . Aspect-based sentiment analysis of syntactic perception and knowledge enhancement; [句法感知与知识增强的方面级情感分析] [J]. | Journal of Xidian University , 2024 , 51 (5) : 165-178 . |
| MLA | Chen, K. 等. "Aspect-based sentiment analysis of syntactic perception and knowledge enhancement; [句法感知与知识增强的方面级情感分析]" . | Journal of Xidian University 51 . 5 (2024) : 165-178 . |
| APA | Chen, K. , Zhang, Y. , Lin, H. . Aspect-based sentiment analysis of syntactic perception and knowledge enhancement; [句法感知与知识增强的方面级情感分析] . | Journal of Xidian University , 2024 , 51 (5) , 165-178 . |
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With the development of e-shopping, a list of similar products can be found with a large volume of valuable customer reviews online. However, it is generally difficult to compare various aspects of similar products effectively by understanding all relevant online opinions. To help consumers, in this study, how products are ranked according to online reviews is investigated. Firstly, an SC-LDA (Seed Constraint-Latent Dirichlet Allocation) model, which is an extension of the classical topic model LDA (Latent Dirichlet Allocation), is proposed to extract product features. The must-link and cannot-link seed constraints are invited to estimate the probability expansion/reduction value. They help to affect the topic allocation by additional constraints in Gibbs sampling for a higher accuracy on feature extraction. Secondly, an improved convolutional memory neural network model is devised to analyze the sentiment polarity. It takes the advantages of CNN (convolutional neural network) and Bi-LSTM (bidirectional Long Short-Term Memory) and performs dynamic pooling in CNN to prevent the loss of important features. Besides, the concept of group satisfaction degree is introduced, which makes products be compared according to the Regret Theory. It ranks products without a commonly applied reference point and take consumer psychology into considerations. Finally, in the case study, an illustrative example is presented to evaluate the proposed framework. Categories of experiments show that the proposed framework provides consumers with effective purchase suggestions. Code metadata: Permanent link to reproducible Capsule: https://doi.org/10.24433/CO.6445683.v1 and https://d oi.org/10.24433/CO.2658577.v1.
Keyword ：
Group satisfaction Group satisfaction Online opinions Online opinions Product ranking Product ranking Purchase suggestions Purchase suggestions Regret theory Regret theory
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| GB/T 7714 | Chen, Kejia , Zheng, Jingjing , Jin, Jian . Ranking products through online opinions: A text analysis and regret theory-based approach [J]. | APPLIED SOFT COMPUTING , 2024 , 158 . |
| MLA | Chen, Kejia 等. "Ranking products through online opinions: A text analysis and regret theory-based approach" . | APPLIED SOFT COMPUTING 158 (2024) . |
| APA | Chen, Kejia , Zheng, Jingjing , Jin, Jian . Ranking products through online opinions: A text analysis and regret theory-based approach . | APPLIED SOFT COMPUTING , 2024 , 158 . |
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In view of the lack of interactive information between aspect words and the context in most aspect-level sentiment studies, and the inability to make full use of semantic information. To address the problems above, a model based on self-attention and graph convolution network is proposed. In order to improve the semantic representation ability of the model, the multi-head self-attention mechanism is used to obtain the long-distance dependency relationship of the text, combined with the dependency type matrix. Then, the weight matrix that combines the location information and the relationship type information is calculated and is inputted to the graph convolution network to obtain the text feature representation. Besides, the text aspect attention layer is employed to extract the context-sensitive aspect features, and it is inputted to graph convolution network to obtain aspect feature representation. Finally, the two vectors above are connected to complete the task of sentiment analysis. Simulation results show that the overall performance of the proposed model is better than that these of other comparison models in two open datasets. © 2024 Beijing University of Posts and Telecommunications. All rights reserved.
Keyword ：
Convolution Convolution Semantics Semantics Semantic Web Semantic Web Sentiment analysis Sentiment analysis
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| GB/T 7714 | Chen, Kejia , Huang, Chunxiang , Lin, Hongxi . Aspect-Level Sentiment Analysis Based on Self-Attention and Graph Convolutional Network [J]. | Journal of Beijing University of Posts and Telecommunications , 2024 , 47 (1) : 127-132 . |
| MLA | Chen, Kejia 等. "Aspect-Level Sentiment Analysis Based on Self-Attention and Graph Convolutional Network" . | Journal of Beijing University of Posts and Telecommunications 47 . 1 (2024) : 127-132 . |
| APA | Chen, Kejia , Huang, Chunxiang , Lin, Hongxi . Aspect-Level Sentiment Analysis Based on Self-Attention and Graph Convolutional Network . | Journal of Beijing University of Posts and Telecommunications , 2024 , 47 (1) , 127-132 . |
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针对当前句法关系研究存在过多考虑主谓关系、情感词识别能力有限、忽视隐式特征提取等方面的不足，提出一种基于句法规则与情感词的隐式特征提取方法。借助中文情感词典资源，基于外部语料与实验语料训练的词向量分别构建混合情感词典和产品特征词典，通过词典和定义的句法规则提取显式特征及情感词，根据其共现关系提取隐式特征。在相机评论语料集上进行实验并与现有方法进行对比，实验结果表明，该方法能有效提取显式及隐式特征，在获取全面特征信息上具有较好的性能。
Keyword ：
产品评论 产品评论 共现分析 共现分析 句法规则 句法规则 情感词 情感词 显式特征 显式特征 词向量 词向量 隐式特征 隐式特征
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| GB/T 7714 | 陈可嘉 , 柯永诚 , 林鸿熙 . 基于句法规则与情感词的隐式特征提取 [J]. | 计算机工程与设计 , 2024 , 45 (03) : 740-747 . |
| MLA | 陈可嘉 等. "基于句法规则与情感词的隐式特征提取" . | 计算机工程与设计 45 . 03 (2024) : 740-747 . |
| APA | 陈可嘉 , 柯永诚 , 林鸿熙 . 基于句法规则与情感词的隐式特征提取 . | 计算机工程与设计 , 2024 , 45 (03) , 740-747 . |
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To address the challenges posed by uncertain passenger demand and airport capacity in the secondary allocation of arrival and departure slots for autonomously canceled flights, we have developed a grey multi-objective linear programming (MOLP) model. The model represents the uncertainty of passenger demand and airport capacity using interval grey numbers. The objective of the model is to minimize the total delay costs of airlines and the total delay time of passengers. Additionally, it takes into account constraints such as wake vortex separation and runway capacity. Furthermore, to reduce the complexity associated with solving the developed model, we propose an improved transformation method based on the concepts of grey center and width. This method streamlines the transformation process by converting the objective functions into expressions for the center and width. It employs the epsilon-constraint method to transform one of the objective functions into a constraint defined by the center and width, thereby minimizing the impact of constraint relaxation on the results. The results of the case study present value ranges for both non-cancellation and cancellation strategies. Additionally, the validation outcomes of the enhanced transformation method fall within the verification range, thereby confirming the model's effectiveness and the reliability of the transformation method. The model broadens the application scope of grey programming, while the proposed transformation method enhances the theoretical framework of this field.
Keyword ：
Center and width Center and width Flight arrival and departure Flight arrival and departure Flight cancellation Flight cancellation Grey multi-objective model Grey multi-objective model Slot secondary allocation Slot secondary allocation
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| GB/T 7714 | Chen, Kejia , Chen, Zhenkun . A Grey Multi-objective Model for Slot Secondary Allocation of Arrival and Departure Flights under Autonomous Cancellation [J]. | JOURNAL OF GREY SYSTEM , 2024 , 36 (6) . |
| MLA | Chen, Kejia 等. "A Grey Multi-objective Model for Slot Secondary Allocation of Arrival and Departure Flights under Autonomous Cancellation" . | JOURNAL OF GREY SYSTEM 36 . 6 (2024) . |
| APA | Chen, Kejia , Chen, Zhenkun . A Grey Multi-objective Model for Slot Secondary Allocation of Arrival and Departure Flights under Autonomous Cancellation . | JOURNAL OF GREY SYSTEM , 2024 , 36 (6) . |
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针对以往大多数方面级情感分析研究中方面词与上下文交互信息缺失，无法充分利用语义信息等问题，提出一种基于自注意力与图卷积网络结合的方面级情感分析模型。为了提高模型的语义表示能力，一方面利用多头自注意力机制，获取文本长距离依赖关系，与依存关系类型矩阵结合，计算融合位置信息和关系类型信息的权重矩阵，输入图卷积网络获取文本特征表示；另一方面设计了文本-方面注意力层，增强方面与上下文的交互，输入图卷积网络获取方面特征表示；最后连接2个向量，完成情感分析任务。在2个开放数据集中，所提模型的整体性能优于其他对比模型。
Keyword ：
图卷积网络 图卷积网络 方面级情感分析 方面级情感分析 自注意力机制 自注意力机制 语义感知 语义感知
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| GB/T 7714 | 陈可嘉 , 黄春香 , 林鸿熙 . 基于自注意力与图卷积网络的方面级情感分析 [J]. | 北京邮电大学学报 , 2024 , 47 (01) : 127-132 . |
| MLA | 陈可嘉 等. "基于自注意力与图卷积网络的方面级情感分析" . | 北京邮电大学学报 47 . 01 (2024) : 127-132 . |
| APA | 陈可嘉 , 黄春香 , 林鸿熙 . 基于自注意力与图卷积网络的方面级情感分析 . | 北京邮电大学学报 , 2024 , 47 (01) , 127-132 . |
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The paper explores the flight recovery problem of flight delay and violation of airport curfew due to the decline of airport capacity under the collaborative decision making. A multi-objective collaborative slot secondary allocation model is proposed to minimise the total delay cost of airlines and the total delay time of passengers. Three multi-objective decision-making (MODM) techniques are introduced, and the displaced ideal solution (DIS) method is used to select the optimal solution technique. The results show that the weighting method (WM) can generate high-quality solutions in the test data set. Finally, combined with the delayed flight data of an airport, LINGO software is used to solve the model, the sensitivity and the complexity is analysed. The results show that the collaborative scheduling strategy proposed in this paper can provide airlines with scientific and reasonable slot secondary allocation scheme under the condition of limited airport capacity. Copyright © 2024 Inderscience Enterprises Ltd.
Keyword ：
Airports Airports Air traffic control Air traffic control Air transportation Air transportation Decision making Decision making Multiobjective optimization Multiobjective optimization Statistical tests Statistical tests
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| GB/T 7714 | Chen, Kejia , Guo, Xiaoqing , Wang, Haiyan . Multi-objective collaborative slot secondary allocation model with curfew restriction [J]. | International Journal of Industrial and Systems Engineering , 2024 , 47 (3) : 311-333 . |
| MLA | Chen, Kejia 等. "Multi-objective collaborative slot secondary allocation model with curfew restriction" . | International Journal of Industrial and Systems Engineering 47 . 3 (2024) : 311-333 . |
| APA | Chen, Kejia , Guo, Xiaoqing , Wang, Haiyan . Multi-objective collaborative slot secondary allocation model with curfew restriction . | International Journal of Industrial and Systems Engineering , 2024 , 47 (3) , 311-333 . |
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To solve the problem of airline flight disruption caused by emergencies, this paper recovers the disrupted departure flight. A bi-objective optimization model for minimizing airline delay cost and passenger delay time is constructed. An adaptive non-dominated sorting genetic algorithm-Ⅱ based on dominant strengths (ANSGA2-DS) is designed. The novel crowding distance, the adaptive elitist retention technique, and the quick dominant sorting approach are the three enhanced operations that are given. The proposed algorithm is verified by the operation data of an airline in Fuzhou Changle International Airport. The experimental results reveal that, compared with the traditional first scheduling first serve method, the algorithm proposed in this paper can reduce the costs greatly. In contrast to the Ε-constraint approach, the Ε-constrained approach requires a longer solution time, and the resulting solution results are similar to those of the Ε-constrained approach. Compared with the NSAG2 algorithm and the MOEAD algorithm, the algorithm proposed in this paper shows better performance. The proposed algorithm can solve the problem effectively and efficiently, and provide a basis for airlines to reach an optimized solution. © 2024 Beijing University of Aeronautics and Astronautics (BUAA). All rights reserved.
Keyword ：
Air transportation Air transportation Genetic algorithms Genetic algorithms Sorting Sorting
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| GB/T 7714 | Chen, Kejia , Wu, Juntao . Improved NSGA2 algorithm for disrupted departure flights recovery [J]. | Journal of Beijing University of Aeronautics and Astronautics , 2024 , 50 (6) : 1784-1793 . |
| MLA | Chen, Kejia 等. "Improved NSGA2 algorithm for disrupted departure flights recovery" . | Journal of Beijing University of Aeronautics and Astronautics 50 . 6 (2024) : 1784-1793 . |
| APA | Chen, Kejia , Wu, Juntao . Improved NSGA2 algorithm for disrupted departure flights recovery . | Journal of Beijing University of Aeronautics and Astronautics , 2024 , 50 (6) , 1784-1793 . |
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多数基于深度学习的情感分类方法过于追求数据驱动,忽视了文本情感特征对于情感分类的影响;而一些融合情感特征的情感分类方法仅利用了部分相关特征,使得其他情感特征并没有得到充分利用.针对这一现象,提出了一种融合词级特征和句级特征的在线评论情感分类模型.首先利用构建的情感元素词典获取情感词、否定词等多种特征词,然后通过多维特征向量表示将多种文本特征转化为词级特征向量和句级特征向量,最后将这些特征向量融入LSTM网络模型完成情感分类,整个模型简称为 MF-LSTM(sentiment classification model based on multidimensional features and LSTM).MF-LSTM 充分利用 了评论文本的情感先验知识,情感分类能力得到显著提升.在3个中文评论数据集上的实验结果表明MF-LSTM模型相比其他深度学习方法具有更好的分类效果,并且在样本数据不平衡的情况下也能具有较好的鲁棒性.
Keyword ：
多特征 多特征 情感分类 情感分类 情感词典 情感词典 深度学习 深度学习 长短时记忆神经网络 长短时记忆神经网络
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| GB/T 7714 | 陈可嘉 , 柯永诚 . 融合多特征的在线评论情感分类 [J]. | 小型微型计算机系统 , 2024 , 45 (5) : 1054-1061 . |
| MLA | 陈可嘉 等. "融合多特征的在线评论情感分类" . | 小型微型计算机系统 45 . 5 (2024) : 1054-1061 . |
| APA | 陈可嘉 , 柯永诚 . 融合多特征的在线评论情感分类 . | 小型微型计算机系统 , 2024 , 45 (5) , 1054-1061 . |
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为解决因突发事件产生的航空公司航班中断问题,对中断的离港航班进行恢复,构建最小化航空公司总延误成本和最小化乘客总延误时间的双目标优化模型,设计基于支配强度的自适应非支配排序遗传算法(ANSGA2-DS).提出3种改进操作:快速支配排序方法、新的拥挤距离和自适应精英保留策略.通过福州长乐国际机场某航空公司的运行数据对所提算法进行验证,实验结果表明:与传统的先规划先服务方法相比,所提算法得到的解有大幅优化;与ε约束法相比,所提算法的求解时间总体上低于ε约束法,且求解结果接近ε约束法所得最优结果;与NSGA2、MOEAD等多目标优化算法相比,所提算法表现出更优的性能,能够有效且高效地解决问题,为航空公司达成优化的解决方案提供基础.
Keyword ：
中断航班恢复 中断航班恢复 双目标优化 双目标优化 多目标遗传算法 多目标遗传算法 离港航班 离港航班 航空运输 航空运输
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| GB/T 7714 | 陈可嘉 , 吴钧涛 . 中断离港航班恢复的改进NSGA2算法 [J]. | 北京航空航天大学学报 , 2024 , 50 (6) : 1784-1793 . |
| MLA | 陈可嘉 等. "中断离港航班恢复的改进NSGA2算法" . | 北京航空航天大学学报 50 . 6 (2024) : 1784-1793 . |
| APA | 陈可嘉 , 吴钧涛 . 中断离港航班恢复的改进NSGA2算法 . | 北京航空航天大学学报 , 2024 , 50 (6) , 1784-1793 . |
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