Back to Search

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

Title	Applying deep learning models to twitter data to detect airport service quality
Author	Barakat, Huda Mohammed Mohammed, Yeniterzi, R., Martin-Domingo, Luis
Publication Date:	2021-03
Publication Place	- Elsevier
Subject	Airport service quality, ASQ, Deep learning, Sentiment analysis, Twitter
Type	Periodical
Language	English
Digital	Yes
Manuscript	No
Library:	Özyeğin University
Library Asset ID	0969-6997
Record ID	559b489f-c097-4df8-ad4d-b0b0a9c6896f
Library Location	Aviation Management
Date	2021-03
Sample Text	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

View in source Özyeğin University Özyeğin University - Historical works, archives, and periodicals search engine

Özyeğin University

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

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

Publication Date 2021-03

Publication Place - Elsevier

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

Type Periodical

Language English

Digital Yes

Manuscript No

Library Özyeğin University

Library Asset ID 0969-6997

Record ID 559b489f-c097-4df8-ad4d-b0b0a9c6896f

Library Location Aviation Management

Date 2021-03

Sample Text 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