Applying deep learning models to twitter data to detect airport service quality

Name: Applying deep learning models to twitter data to detect airport service quality
Author: Barakat, Huda Mohammed Mohammed, Yeniterzi, R., Martin-Domingo, Luis

İsim	Applying deep learning models to twitter data to detect airport service quality
Yazar	Barakat, Huda Mohammed Mohammed, Yeniterzi, R., Martin-Domingo, Luis
Basım Tarihi:	2021-03
Basım Yeri	- Elsevier
Konu	Airport service quality, ASQ, Deep learning, Sentiment analysis, Twitter
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	0969-6997
Kayıt Numarası	559b489f-c097-4df8-ad4d-b0b0a9c6896f
Lokasyon	Aviation Management
Tarih	2021-03
Örnek Metin	Measuring airport service quality (ASQ) is an important process for identifying shortages and suggesting improvements that guide management decisions. This research, introduces a general framework for measuring ASQ using passengers’ tweets about airports. The proposed framework considers tweets in any language, not just in English, to support ASQ evaluation in non-speaking English countries where passengers communicate with other languages. Accordingly, this work uses a large dataset that includes tweets in two languages (English and Arabic) and from four airports. Additionally, to extract passenger evaluations from tweets, our framework applies two different deep learning models (CNN and LSTM) and compares their results. The two models are trained with both general data and data from the aviation domain in order to clarify the effect of data type on model performance. Results show that better performance is achieved with the LSTM model when trained with domain specific data. This study has clear implications for researchers and airport managers aiming to use alternative methods to measure ASQ.
DOI	10.1016/j.jairtraman.2020.102003
Cilt	91

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Applying deep learning models to twitter data to detect airport service quality

Yazar Barakat, Huda Mohammed Mohammed, Yeniterzi, R., Martin-Domingo, Luis

Basım Tarihi 2021-03

Basım Yeri - Elsevier

Konu Airport service quality, ASQ, Deep learning, Sentiment analysis, Twitter

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 0969-6997

Kayıt Numarası 559b489f-c097-4df8-ad4d-b0b0a9c6896f

Lokasyon Aviation Management

Tarih 2021-03

Örnek Metin Measuring airport service quality (ASQ) is an important process for identifying shortages and suggesting improvements that guide management decisions. This research, introduces a general framework for measuring ASQ using passengers’ tweets about airports. The proposed framework considers tweets in any language, not just in English, to support ASQ evaluation in non-speaking English countries where passengers communicate with other languages. Accordingly, this work uses a large dataset that includes tweets in two languages (English and Arabic) and from four airports. Additionally, to extract passenger evaluations from tweets, our framework applies two different deep learning models (CNN and LSTM) and compares their results. The two models are trained with both general data and data from the aviation domain in order to clarify the effect of data type on model performance. Results show that better performance is achieved with the LSTM model when trained with domain specific data. This study has clear implications for researchers and airport managers aiming to use alternative methods to measure ASQ.

DOI 10.1016/j.jairtraman.2020.102003

Cilt 91