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- 航空旅行轮椅设计，行动不便者的福音
- 作者： Amer Siddiqui Ali Asgar Salim 来源： 工业设计 年份： 2024 文献类型 ： 期刊
- 描述： <正>这是一种新型的航空旅行轮椅，可以帮助行动不便的乘客方便快速地登机和入座。一般来说，乘坐轮椅的乘客在登机后必须转移到飞机上的固定座位，这一过程复杂而艰难。使用该航空旅行轮椅后，这一流程将更加便利，行动不便的乘客可以在从登机口到抵达大厅的整个行程中不需要更换座位。
- Transforming Customer Experience in the Airline Industry: A Comprehensive Analysis of Twitter Sentiments Using Machine Learning and Association Rule Mining
- 作者： Maliha Tayaba Eftekhar Hossain Ayon Md Tuhin Mia Malay Sarkar Rejon Kumar Ray Md Salim Chowdhury Md Al Imran Nur Nobe Bishnu Padh Ghosh MD Tanvir Islam Aisharyja Roy Puja 来源： Journal of Computer Science and Technology Studies 年份： 2023 文献类型 ： 期刊
- 描述： The airline industry places significant emphasis on improving customer experience, and Twitter has emerged as a key platform for passengers to share their opinions. This research introduces a machine learning approach to analyze tweets and enhance customer experience. Features are extracted from tweets using both the Glove dictionary and n-gram methods for word embedding. The study explores various artificial neural network (ANN) architectures and Support Vector Machines (SVM) to create a classification model for categorizing tweets into positive and negative sentiments. Additionally, a Convolutional Neural Network (CNN) is developed for tweet classification, and its performance is compared with the most accurate model identified among SVM and multiple ANN architectures. The results indicate that the CNN model surpasses the SVM and ANN models. To provide further insights, association rule mining is applied to different tweet categories, revealing connections with sentiment categories. These findings offer valuable information to help airline industries refine and enhance their customer experience strategies.
- Transforming Customer Experience in the Airline Industry: A Comprehensive Analysis of Twitter Sentiments Using Machine Learning and Association Rule Mining
- 作者： Maliha Tayaba Eftekhar Hossain Ayon Md Tuhin Mia Malay Sarkar Rejon Kumar Ray Md Salim Chowdhury Md Al Imran Nur Nobe Bishnu Padh Ghosh MD Tanvir Islam Aisharyja Roy Puja 来源： Journal of Computer Science and Technology Studies 年份： 2023 文献类型 ： 期刊
- 描述： The airline industry places significant emphasis on improving customer experience, and Twitter has emerged as a key platform for passengers to share their opinions. This research introduces a machine learning approach to analyze tweets and enhance customer experience. Features are extracted from tweets using both the Glove dictionary and n-gram methods for word embedding. The study explores various artificial neural network (ANN) architectures and Support Vector Machines (SVM) to create a classification model for categorizing tweets into positive and negative sentiments. Additionally, a Convolutional Neural Network (CNN) is developed for tweet classification, and its performance is compared with the most accurate model identified among SVM and multiple ANN architectures. The results indicate that the CNN model surpasses the SVM and ANN models. To provide further insights, association rule mining is applied to different tweet categories, revealing connections with sentiment categories. These findings offer valuable information to help airline industries refine and enhance their customer experience strategies.