公共交通用户满意度分析的自然语言处理框架:一个案例研究

Buket Capali, E. Küçüksille, Nazan KEMALOĞLU ALAGÖZ
{"title":"公共交通用户满意度分析的自然语言处理框架:一个案例研究","authors":"Buket Capali, E. Küçüksille, Nazan KEMALOĞLU ALAGÖZ","doi":"10.53635/jit.1274928","DOIUrl":null,"url":null,"abstract":"Public transportation services make an important contribution to the nation's economy. However, the public transportation system was significantly impacted both during and after the Covid-19 outbreak. To minimize these impacts, it is important to know the users' sentiment and improve the service quality accordingly to change the users' attitude towards public transportation systems. Natural language processing is used to make meaningful inferences about user sentiment using various analysis techniques. Historically, surveys have also been used for years to learn users' opinions about transportation services. In this study, this traditional method was used to determine the satisfaction of public transportation users. The categorization model employed in the system developed as part of this work is based on algorithms such as Long Short-Term Memory (LSTM), Random Forest (RF), and Multi Logistic Regression (MLR). The dataset contains information gathered from the online survey. Of the models created utilizing the training dataset, it was discovered that the LSTM model offered the highest accuracy. Users' comments can help improve public transportation operators' operations, improve service quality, and monitor actions accordingly. Therefore, in this study, users' emotions were classified as positive, negative, or neutral based on the comments.","PeriodicalId":192007,"journal":{"name":"Journal of Innovative Transportation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A natural language processing framework for analyzing public transportation user satisfaction: a case study\",\"authors\":\"Buket Capali, E. Küçüksille, Nazan KEMALOĞLU ALAGÖZ\",\"doi\":\"10.53635/jit.1274928\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Public transportation services make an important contribution to the nation's economy. However, the public transportation system was significantly impacted both during and after the Covid-19 outbreak. To minimize these impacts, it is important to know the users' sentiment and improve the service quality accordingly to change the users' attitude towards public transportation systems. Natural language processing is used to make meaningful inferences about user sentiment using various analysis techniques. Historically, surveys have also been used for years to learn users' opinions about transportation services. In this study, this traditional method was used to determine the satisfaction of public transportation users. The categorization model employed in the system developed as part of this work is based on algorithms such as Long Short-Term Memory (LSTM), Random Forest (RF), and Multi Logistic Regression (MLR). The dataset contains information gathered from the online survey. Of the models created utilizing the training dataset, it was discovered that the LSTM model offered the highest accuracy. Users' comments can help improve public transportation operators' operations, improve service quality, and monitor actions accordingly. Therefore, in this study, users' emotions were classified as positive, negative, or neutral based on the comments.\",\"PeriodicalId\":192007,\"journal\":{\"name\":\"Journal of Innovative Transportation\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-05-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Innovative Transportation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.53635/jit.1274928\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Innovative Transportation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.53635/jit.1274928","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

公共交通服务对国家经济做出了重要贡献。然而,在新冠疫情爆发期间和之后,公共交通系统都受到了重大影响。为了最大限度地减少这些影响,了解用户的情绪,提高服务质量,从而改变用户对公共交通系统的态度是很重要的。自然语言处理使用各种分析技术对用户情感进行有意义的推断。从历史上看,调查也被用于多年来了解用户对交通服务的看法。在本研究中,使用传统的方法来确定公共交通用户的满意度。作为这项工作的一部分,系统中使用的分类模型基于长短期记忆(LSTM)、随机森林(RF)和多元逻辑回归(MLR)等算法。该数据集包含从在线调查中收集的信息。在利用训练数据集创建的模型中,发现LSTM模型提供了最高的准确性。用户的评论可以帮助公共交通运营商改善运营,提高服务质量,并对相应的行动进行监控。因此,在本研究中,根据用户的评论将用户的情绪分为积极、消极和中性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A natural language processing framework for analyzing public transportation user satisfaction: a case study
Public transportation services make an important contribution to the nation's economy. However, the public transportation system was significantly impacted both during and after the Covid-19 outbreak. To minimize these impacts, it is important to know the users' sentiment and improve the service quality accordingly to change the users' attitude towards public transportation systems. Natural language processing is used to make meaningful inferences about user sentiment using various analysis techniques. Historically, surveys have also been used for years to learn users' opinions about transportation services. In this study, this traditional method was used to determine the satisfaction of public transportation users. The categorization model employed in the system developed as part of this work is based on algorithms such as Long Short-Term Memory (LSTM), Random Forest (RF), and Multi Logistic Regression (MLR). The dataset contains information gathered from the online survey. Of the models created utilizing the training dataset, it was discovered that the LSTM model offered the highest accuracy. Users' comments can help improve public transportation operators' operations, improve service quality, and monitor actions accordingly. Therefore, in this study, users' emotions were classified as positive, negative, or neutral based on the comments.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信