A natural language processing framework for analyzing public transportation user satisfaction: a case study

Buket Çapalı; Ecir Küçüksille; Nazan Kemaloğlu Alagöz

Makale detayları

A natural language processing framework for analyzing public transportation user satisfaction: a case study

Yazarlar:

Buket Çapalı | Ecir Küçüksille | Nazan Kemaloğlu Alagöz

Yayıncı:

Süleyman Demirel University

Ana Başlıklar:

Transportation Engineering

Dergi:

Journal of Innovative Transportation Vol.4 Issue 1, pp.17-24

Belge Tipi:

Makale

Yayın Tarihi:

7/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001293244

pISSN:

2717-8889

Anahtar kelimeler:

Özet:

 Public transportation services make an important contribution to the nations 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.

 Public transportation services make an important contribution to the nations 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.

 Public transportation services make an important contribution to the nations 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.

 Public transportation services make an important contribution to the nations 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.

Makale detayları

A natural language processing framework for analyzing public transportation user satisfaction: a case study

Yazarlar:

Buket Çapalı | Ecir Küçüksille | Nazan Kemaloğlu Alagöz

Yayıncı:

Süleyman Demirel University

Ana Başlıklar:

Transportation Engineering

Dergi:

Journal of Innovative Transportation Vol.4 Issue 1, pp.17-24

Belge Tipi:

Makale

Yayın Tarihi:

7/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001293244

pISSN:

2717-8889

Public transportation

service quality

natural language processing

sentiment analysis

machine learning

Sayı

Makale detayları

A natural language processing framework for analyzing public transportation user satisfaction: a case study

Yazarlar: Buket Çapalı | Ecir Küçüksille | Nazan Kemaloğlu Alagöz

Yayıncı: Süleyman Demirel University

Ana Başlıklar: Transportation Engineering

Dergi: Journal of Innovative Transportation Vol.4 Issue 1, pp.17-24

Belge Tipi: Makale

Yayın Tarihi: 7/2023

Belge Dili: İngilizce

Document ID (Org No.): 2001293244

pISSN: 2717-8889

Public transportation

service quality

natural language processing

sentiment analysis

machine learning

Sayı

Yazarlar:

Buket Çapalı | Ecir Küçüksille | Nazan Kemaloğlu Alagöz

Yayıncı:

Süleyman Demirel University

Ana Başlıklar:

Transportation Engineering

Dergi:

Journal of Innovative Transportation Vol.4 Issue 1, pp.17-24

Belge Tipi:

Makale

Yayın Tarihi:

7/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001293244

pISSN:

2717-8889