Identification of opinion trends using sentiment analysis of airlines passengers' reviews

Farzadnia, Siavash; Vanani, Iman Raeesi

doi:10.1016/j.jairtraman.2022.102232

Cited by 14 publications

(4 citation statements)

References 31 publications

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“…In predicting airline fares, the typical factors, including season, travel day and month, weekend, holiday, booking class, class of service, and number of stops, are taken into consideration [6,9,18]. However, passenger sentiments on key services may also affect the ticket prices [37][38][39]. With ABSA, airlines can discern the specific service attributes that might determine travelers' decisions of expenditure [39,40].…”

Section: Aspect-based Sentiment Analysismentioning

confidence: 99%

“…However, passenger sentiments on key services may also affect the ticket prices [37][38][39]. With ABSA, airlines can discern the specific service attributes that might determine travelers' decisions of expenditure [39,40]. By combining this knowledge with historical fare data, the prediction models may become more refined and can capture those aspects of passengers' experiences that influence fare choices.…”

Section: Aspect-based Sentiment Analysismentioning

confidence: 99%

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A Multi-Aspect Informed GRU: A Hybrid Model of Flight Fare Forecasting with Sentiment Analysis

Degife,

Lin

2024

Applied Sciences

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This paper presents an advanced method for forecasting flight fares that combines aspect-based sentiment analysis (ABSA) with deep learning techniques, particularly the gated recurrent unit (GRU) model. This approach leverages historical airline ticket transaction data and customer reviews to better understand airline fare dynamics and the impact of customer sentiments on pricing. The aspect analysis extracts key service aspects from customer feedback and provides insightful correlations with airfare. These were further categorized into nine groups for sensitivity analysis, which offered a deeper understanding of how each group influences customers’ attitudes. This ABSA-driven forecasting method marks a departure from traditional models by utilizing sentiment data alongside airline transaction data to improve the predictive accuracy. Its effectiveness is demonstrated through metrics including a root mean square error (RMSE) of 0.0071, a mean absolute error (MAE) of 0.0137, and a coefficient of determination (R2) of 0.9899. Additionally, this model shows strong prediction performance in both short- and long-term fare predictions. It not only advances airfare forecasting methods but provides valuable insights for decision makers of airline industry to refine the pricing strategies or make improvements when it is indicated some services require further attention.

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Section: Aspect-based Sentiment Analysismentioning

confidence: 99%

Section: Aspect-based Sentiment Analysismentioning

confidence: 99%

A Multi-Aspect Informed GRU: A Hybrid Model of Flight Fare Forecasting with Sentiment Analysis

Degife,

Lin

2024

Applied Sciences

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“…The process offers a systematic and comprehensive approach to sentiment analysis in airline services. By utilizing sentiment analysis, airlines can gain valuable insights into customer preferences and needs (Farzadnia and Raeesi Vanani, 2022;Patel et al, 2023). They can also monitor service performance, assess customer satisfaction levels (Farzadnia and Raeesi Vanani, 2022), evaluate competitive positions by identifying strengths and weaknesses (Patel et al, 2023), and formulate effective strategies to enhance service quality and foster customer loyalty.…”

Section: Sentiment Insights and Decision-makingmentioning

confidence: 99%

A Study on Sentiment Analysis on Airline Quality Services: A Conceptual Paper

Idris,

Mohamad

2023

IMBR

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Airline quality service is crucial for airlines to remain competitive in the industry. The quality of the services of these airlines must meet customer satisfaction and other aspects of the overall service experience. The levels of service quality in an airline service may impact satisfaction and loyalty which may influence customer sentiment. Concerning the importance of airline quality service, customer sentiment towards the service must be investigated and one of the ways to analyze it is by using sentiment analysis. Sentiment analysis is the chosen tool nowadays to analyze comments or reviews made on these services, which may be positive, negative, or neutral. Using sentiment analysis, will not only help potential customers to view the overall sentiment portrayed, but organizations can also use the findings to improve their organization to be more competitive. Thus, this paper will focus on reviewing several recent works related to sentiment analysis as a tool for assisting organizations in assessing the quality of services in the airline industry. As a result, a new framework for assessing the quality of service for the organizations, especially the airline company will be proposed.

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“…Teknologi Big Data berkembang dalam pengelolaan data dengan jumlah yang sangat besar dan kompleks sehingga dapat membantu dalam mendapatkan berbagai wawasan, informasi, solusi; serta meningkatkan keunggulan kompetitif yang signifikan di sebagian besar organisasi, perusahaan, institusi, dan lain-lain [6]. Hasil penelitian sebelumnya terkait dengan pemanfaatan Big Data dalam meningkatkan kualitas maskapai penerbangan menunjukkan bahwa analisis sentimen pada ulasan penumpang maskapai dapat digunakan untuk mendapatkan informasi mengenai kekuatan dan kelemahan pelayanan maskapai kepada pelanggan sehingga maskapai tersebut dapat mengembangkan strategi baru dan meningkatkan pangsa pasar mereka [7]. Penelitian sebelumnya yang terkait dengan analisis sentimen berhasil menggunakan metode Multinomial Naïve Bayes untuk mengklasifikasi sentimen pada data review produk di situs Lazada dengan hasil nilai accuracy, precision, recall, dan f1-score sebesar 90% [8].…”

Section: Pendahuluanunclassified

Analisis Sentimen Masyarakat terhadap Brand Lokal di E-Commerce dengan Pendekatan Deep Learning Menggunakan Metode XLNet

Wiartha,

Suarjaya,

Rusjayanthi

2023

jitter

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Pertumbuhan perekonomian Indonesia dapat didongkrak dengan meningkatkan minat masyarakat untuk membeli produk brand lokal. Brand awareness, brand image, dan kualitas produk yang baik dapat memengaruhi minat masyarakat dalam membeli produk lokal. Teknologi Big Data dapat dimanfaatkan untuk menganalisis sentimen masyarakat melalui data review produk brand lokal untuk mengetahui citra dan kualitas produk brand lokal. Penelitian yang dilakukan oleh penulis bertujuan untuk mengetahui sentimen masyarakat terhadap brand lokal berdasarkan data review produknya di situs e-commerce Tokopedia, Shopee, Blibli, Bukalapak, Lazada, dan Zalora. Metode yang digunakan pada penelitian yaitu XLNet. Penelitian yang dilakukan oleh penulis memperoleh data review sebanyak 16.663.045 review dengan jumlah data yang telah diproses sebanyak 4.110.012 review serta dataset training sebanyak 11.606 review yang terdiri dari 5803 data review positif dan 5803 data review negatif. Hasil analisis sentimen terhadap produk brand lokal di situs e-commerce menunjukkan bahwa data review positif lebih dominan (87,82%) dibandingkan data review negatif (12,18%) sehingga dapat disimpulkan bahwa masyarakat Indonesia cenderung puas dan menyukai produk brand lokal yang tersedia di e-commerce. Hasil performa metode XLNet menunjukkan nilai accuracy sebesar 95,6%; precision sebesar 95,06%; recall sebesar 96,21%; dan f1-score sebesar 95,63%.

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Identification of opinion trends using sentiment analysis of airlines passengers' reviews

Cited by 14 publications

References 31 publications

A Multi-Aspect Informed GRU: A Hybrid Model of Flight Fare Forecasting with Sentiment Analysis

A Multi-Aspect Informed GRU: A Hybrid Model of Flight Fare Forecasting with Sentiment Analysis

A Study on Sentiment Analysis on Airline Quality Services: A Conceptual Paper

Analisis Sentimen Masyarakat terhadap Brand Lokal di E-Commerce dengan Pendekatan Deep Learning Menggunakan Metode XLNet

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