The middle east 3 : a sentiment analysis on airline customer reviews

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Marketing IntelligenceAlong with the exponential growth of social media, the world has taken a turn, and we are no longer limited to the knowledge of our network of friends and family. Word-of-Mouth became especially relevant since travel services are intangible products and when the customers are unfamiliar with a service provider they rely on sources with experience to lower their scepticism. Online reviews are important sources of the consumer experience that can be explored to get valuable insights. Sentiment analysis has been applied to almost any field of study including tourism and hospitality. The Airline industry revenues come mostly from air passengers, and the most significant impact of research on airline service quality comes from the combination of the customer’s real experience and satisfaction. This dissertation has the goal to understand the polarity distribution on the aspects that influenced the three biggest Middle Eastern airlines customer’s satisfaction from 2014 to 2016, on Skytrax and if that polarity found on Skytrax matches the one found on TripAdvisor for 2016. The database was extracted with a web scraper and analysed with Excel Add-in from MeaningCloud. In-flight Entertainment revealed to be the aspect with the most positive sentiment for Emirates and Etihad Airways, while for Qatar Airways the strength is on the Employees aspect. The Convenience of the Flight Schedule was an issue for the reviewers regardless of the airline

The impact of the COVID 19 pandemic on European airlines' passenger satisfaction

The COVID-19 pandemic brought many challenges to the airline industry, resulting in radical changes to the passengers’ experience. The purpose of this study is to understand the differences in customer satisfaction between the pre-COVID-19 period and during the COVID-19 pandemic, as well the factors that influence said satisfaction. The sample of this study consists of a dataset with 9,745 reviews written by passengers on the well-known airline reviews website, airlinequality.com, owned by SKYTRAX. The reviews were analyzed with a sentiment analysis tool that was specially calibrated for the aviation industry to be more accurate. The findings of this study show that passengers were unhappy with airlines before the pandemic, and those feelings were aggravated after the COVID-19 outbreak. The behavior of airline staff is the main factor to influence passengers’ satisfaction. The main takeaway is that passengers, after the pandemic, are mostly worried with refunds and aircraft cabin cleanliness. This study shows that analyzing passenger reviews is an effective way of gathering customer feedback, paving the way for airlines to continuously improve their service offerings.A pandemia COVID-19 trouxe muitos desafios à indústria da aviação, resultando em drásticas alterações à experiência dos passageiros. O objetivo deste estudo é compreender as diferenças na satisfação dos passageiros, antes e depois da pandemia COVID-19, bem como quais os fatores que a influenciam. A amostra consiste em 9745 comentários deixados por passageiros no conhecido site de comentários, airlinequality.com, cujo proprietário é a SKYTRAX. Os comentários foram analisados recorrendo a uma ferramenta de análise de sentimentos, especialmente calibrada para a indústria aeronáutica, de modo a obter resultados mais precisos. Os resultados sugerem que os passageiros não estavam satisfeitos com as companhias aéreas, e esse sentimento foi agravado durante a pandemia. O comportamento dos trabalhadores das companhias aéreas são o fator que mais influencia a satisfação dos passageiros. A principal conclusão é que os passageiros, após a pandemia, demonstram preocupações acrescidas com reembolsos e com a limpeza da cabine das aeronaves. Este estudo mostra que análise de comentários de passageiros é uma forma eficiente de recolher a opinião dos clientes, dando oportunidade às companhias aéreas de melhorarem continuamente os seus serviços

Unleashing the Power of User Reviews: Exploring Airline Choices at Catania Airport, Italy

This study aims to investigate the possible relationship between the mechanisms of social influence and the choice of airline, through the use of new tools, with the aim of understanding whether they can contribute to a better understanding of the factors influencing the decisions of consumers in the aviation sector. We have chosen to extract user reviews from well-known platforms: Trustpilot, Google, and Twitter. By combining web scraping techniques, we have been able to collect a comprehensive dataset comprising a wide range of user opinions, feedback, and ratings. We then refined the BERT model to focus on insightful sentiment in the context of airline reviews. Through our analysis, we observed an intriguing trend of average negative sentiment scores across various airlines, giving us deeper insight into the dynamics between airlines and helping us identify key partnerships, popular routes, and airlines that play a central role in the aeronautical ecosystem of Catania airport during the specified period. Our investigation led us to find that, despite an airline having received prestigious awards as a low-cost leader in Europe for two consecutive years 2021 and 2022, the "Catanese" user tends to suffer the dominant position of other companies. Understanding the impact of positive reviews and leveraging sentiment analysis can help airlines improve their reputation, attract more customers, and ultimately gain a competitive edge in the marketplace.Comment: arXiv admin note: text overlap with arXiv:1311.3475 by other author

The impact of the COVID-19 pandemic on airlines’ passenger satisfaction

This study aims to understand airline passengers' satisfaction trends by analyzing the most influential factors on satisfaction before and during the COVID-19 pandemic. The sample consists of a dataset with 9745 passenger reviews published on airlinequality.com. The reviews were analyzed with a sentiment analysis tool calibrated for the aviation industry for accuracy. Machine learning algorithms were then implemented to predict review sentiment based on airline company, travelers' type and class, and country of origin. Findings show passengers were unhappy before the pandemic, aggravated after the COVID-19 outbreak. The staff's behavior is the main factor influencing passengers' satisfaction. Predictive modeling showed that it is possible to predict negative review sentiments with satisfactory performance rather than positive reviews. The main takeaway is that passengers, after the pandemic, are most worried about refunds and aircraft cabin cleanliness. From a managerial standpoint, airline companies can benefit from the created knowledge to adjust their strategies in agreement and meet their customers' expectations.info:eu-repo/semantics/publishedVersio

Monitoring Airport Service Quality:A Complementary Approach to Measure Perceived Service Quality using Online Reviews

Based on 42,063 airport reviews collected from Google Maps, we conducted a sentiment analysis and a topic modeling. We showed that the sentiment scores computed from textual reviews are good estimates of their paired star-ratings (r=0.63, p\u3c0.01). Next, using the LDA (Latent Dirichlet Allocation), we extracted latent topics from the textual reviews and compared them with the standard categories utilized in the Airport Service Quality survey (ASQ). The topics extracted from reviews correspond well with the categories used in ASQ. We, in turn, compared the online ratings with the ratings annually updated by ASQ. While online reviews discuss almost identical topics with those of ASQ, the correlation between the ratings from two was weak (r=0.2). We suggest that the text mining approach using online reviews not only provides an inexpensive, dynamic, and locally customizable means of monitoring airport quality but also complements the standard survey by offering an alternative metric

Use of Electronic Word of Mouth as Quality Metrics: A Comparison of Airline Reviews on Twitter and Skytrax

User-generated content (UGC) at online platforms serves as a critical data source in the service industry as it can be accessed in real-time and reflect customers’ changing focus on service aspects. Drawing upon the importance-performance analysis framework, we propose a methodology to derive service quality metrics by utilizing the heterogeneous sources of UGC with customized text mining techniques and examining the effectiveness of these quality metrics. UGC data related to major U.S. airlines were collected from non-social media (Skytrax) and social media platforms (Twitter) from 2014 to June 2019. The results suggest that the topic distributions and the UGC-derived weighted service quality (WSQ, which represents the weighted sentiment based on service aspects) significantly vary between the non-social media and social media platforms. In addition, the WSQ scores derived from two platforms are significant indicators of the objective service quality measurement (i.e., airline quality rating) with stronger predictive power from the social media derived WSQ score