A Comparative Sentiment Analysis of Airline Customer Reviews Using Bidirectional Encoder Representations from Transformers (BERT) and Its Variants

作者： Zehong   Li     Chuyang   Yang     Chenyu   Huang   来源： Mathematics 年份： 2023 文献类型 ： 期刊 关键词： sentiment   natural   Airline   Analysis   62P25   Service   processing   language   learning   Machine   customer  

描述： The applications of artificial intelligence (AI) and natural language processing (NLP) have significantly empowered the safety and operational efficiency within the aviation sector for safer and more efficient operations. Airlines derive informed decisions to enhance operational efficiency and strategic planning through extensive contextual analysis of customer reviews and feedback from social media, such as Twitter and Facebook. However, this form of analytical endeavor is labor-intensive and time-consuming. Extensive studies have investigated NLP algorithms for sentiment analysis based on textual customer feedback, thereby underscoring the necessity for an in-depth investigation of transformer architecture-based NLP models. In this study, we conducted an exploration of the large language model BERT and three of its derivatives using an airline sentiment tweet dataset for downstream tasks. We further honed this fine-tuning by adjusting the hyperparameters, thus improving the model’s consistency and precision of outcomes. With RoBERTa distinctly emerging as the most precise and overall effective model in both the binary (96.97%) and tri-class (86.89%) sentiment classification tasks and persisting in outperforming others in the balanced dataset for tri-class sentiment classification, our results validate the BERT models’ application in analyzing airline industry customer sentiment. In addition, this study identifies the scope for improvement in future studies, such as investigating more systematic and balanced datasets, applying other large language models, and using novel fine-tuning approaches. Our study serves as a pivotal benchmark for future exploration in customer sentiment analysis, with implications that extend from the airline industry to broader transportation sectors, where customer feedback plays a crucial role.

Research on Noncollinear Shear Wave Mixing Technology for Fatigue Crack Detection and Visualization of Aerospace Titanium Alloys

作者： Yuhua   Zhang       Silong   Quan       Yuezhong   Li   来源： Journal of Materials Engineering and Performance 年份： 2023 文献类型 ： 期刊 关键词： damage   visualization   alloy   shear   mixing   invisible   nonlinear   wave   fatigue   crack   noncollinear   Titanium   coefficient  

描述： In this study, we apply the noncollinear shear wave mixing technique to detect and visualize fatigue damage in aerospace titanium alloy components that are invisible to conventional methods. This technique exploits the nonlinear interaction between two columns of shear waves and fatigue damage in titanium alloy components, which generates a sum-frequency longitudinal wave. By adjusting the relative position and separation distance of the excitation transducers and the receiving transducer in the nonlinear mixing system, we can effectively control the mixing beam in the horizontal/vertical direction of the detection position. We test the nonlinear characteristics of 63 detection points near the fatigue crack. The experimental results show that the mixing nonlinear coefficient exhibits a “step” trend when the fatigue crack is inside the titanium alloy, and the “step” width increases with the crack length. The distribution of the mixing nonlinear coefficient can be used to effectively characterize the length of invisible fatigue crack in the vertical direction. Moreover, we can visualize the fatigue damage area near the invisible fatigue crack inside the metal component by using the contour map of the normalized mixing nonlinear coefficient of 63 detection points near the fatigue crack.

Application of low-altitude wind shear recognition algorithm and laser wind radar in aviation meteorological services

作者： Xu   Ting     Ma   Xiaoling     Li   Qionghua     Lu   Yan   来源： Open Physics 年份： 2023 文献类型 ： 期刊 关键词： Squares   shear   laser   Aviation   Radar   least   k   neighborhood   fitting   wind   frequency  

描述： on sunny, cloudy, and fog days are 0.978, 0.954, and 0.961, respectively. And the correlation

Estimating Soil Carbon Sequestration of Jatropha for Sustainable Aviation Fuel Pathway

作者： Zhang   Zongwei     Li   Junqi     Wang   Zihan     Liu   Haonan     Wei   Keheng   来源： Water, Air, & Soil Pollution 年份： 2023 文献类型 ： 期刊 关键词： emission   Oil   Aviation   use   sequestration   carbon   Fuel   Sustainable   Land   Jatropha  

描述： two methods for producing Jatropha oil–based aviation kerosene (pathway 1: oil residue used for

Hypergraph convolution mix DDPG for multi-aerial base station deployment

作者： He   Haoran     Zhou   Fanqin     Zhao   Yikun     Li   Wenjing     Feng   Lei   来源： Journal of Cloud Computing 年份： 2023 文献类型 ： 期刊 关键词： Hypergraph   Agent   deep   efficiency   (AeBS)   learning   decomposition   aerial   (HGCN)   multi   (MADRL)   optimization   station   Value   convolution   reinforcement   base   energy  

描述： Aerial base stations (AeBS), as crucial components of air-ground integrated networks, can serve as the edge nodes to provide flexible services to ground users. Optimizing the deployment of multiple AeBSs to maximize system energy efficiency is currently a prominent and actively researched topic in the AeBS-assisted edge-cloud computing network. In this paper, we deploy AeBSs using multi-agent deep reinforcement learning (MADRL). We describe the multi-AeBS deployment challenge as a decentralized partially observable Markov decision process (Dec-POMDP), taking into consideration the constrained observation range of AeBSs. The hypergraph convolution mix deep deterministic policy gradient (HCMIX-DDPG) algorithm is designed to maximize the system energy efficiency. The proposed algorithm uses the value decomposition framework to solve the lazy agent problem, and hypergraph convolutional (HGCN) network is introduced to strengthen the cooperative relationship between agents. Simulation results show that the suggested HCMIX-DDPG algorithm outperforms alternative baseline algorithms in the multi-AeBS deployment scenario.

Self-Adaptive-Filling Deep Convolutional Neural Network Classification Method for Mountain Vegetation Type Based on High Spatial Resolution Aerial Images

作者： Shiou   Li     Xianyun   Fei     Peilong   Chen     Zhen   Wang     Yajun   Gao     Kai   Cheng     Huilong   Wang     Yuanzhi   Zhang   来源： Remote Sensing 年份： 2023 文献类型 ： 期刊 关键词： based   image   sensing   deep   images   vegetation   learning   type   classification   aerial   remote   mountain   high   Analysis   spatial   object  

描述： The composition and structure of mountain vegetation are complex and changeable, and thus urgently require the integration of Object-Based Image Analysis (OBIA) and Deep Convolutional Neural Networks (DCNNs). However, while integration technology studies are continuing to increase, there have been few studies that have carried out the classification of mountain vegetation by combining OBIA and DCNNs, for it is difficult to obtain enough samples to trigger the potential of DCNNs for mountain vegetation type classification, especially using high-spatial-resolution remote sensing images. To address this issue, we propose a self-adaptive-filling method (SAF) to incorporate the OBIA method to improve the performance of DCNNs in mountain vegetation type classification using high-spatial-resolution aerial images. Using this method, SAF technology was employed to produce enough regular sample data for DCNNs by filling the irregular objects created by image segmenting using interior adaptive pixel blocks. Meanwhile, non-sample segmented image objects were shaped into different regular rectangular blocks via SAF. Then, the classification result was defined by voting combining the DCNN performance. Compared to traditional OBIA methods, SAF generates more samples for the DCNN and fully utilizes every single pixel of the DCNN input. We design experiments to compare them with traditional OBIA and semantic segmentation methods, such as U-net, MACU-net, and SegNeXt. The results show that our SAF-DCNN outperforms traditional OBIA in terms of accuracy and it is similar to the accuracy of the best performing method in semantic segmentation. However, it reduces the common pretzel phenomenon of semantic segmentation (black and white noise generated in classification). Overall, the SAF-based OBIA using DCNNs, which is proposed in this paper, is superior to other commonly used methods for vegetation classification in mountainous areas.