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

Barakat, H. and Yeniterzi, Reyyan and Martín-Domingo, L. (2021) Applying deep learning models to twitter data to detect airport service quality. Journal of Air Transport Management, 91 . ISSN 0969-6997 (Print) 1873-2089 (Online)

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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.
Item Type: Article
Uncontrolled Keywords: Airport service quality; ASQ; Deep learning; Sentiment analysis; Twitter
Divisions: Faculty of Engineering and Natural Sciences > Academic programs > Computer Science & Eng.
Faculty of Engineering and Natural Sciences
Depositing User: Reyyan Yeniterzi
Date Deposited: 12 Aug 2022 15:43
Last Modified: 12 Aug 2022 15:43

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