A sentiment analysis framework to classify instances of sarcastic sentiments within the aviation sector

Social media in our current dispensation has become an integral part of daily routines. As a result, it is abundant in user opinions. Amid a global pandemic, these online platforms have taken a center stage in the disbursement of relevant information such as travel, emergency and pandemic hotspots. For researchers, this situation has presented itself as a challenge and opportunity to leverage big data for analysis and making informed decisions. This study seeks to develop a framework comprising of three operators, namely Assemble+Deft, Edify+Authenticate and Forecast to classify opinion instances as sarcastic or non-sarcastic. The framework is tested with a Twitter dataset using key state-of-the-art techniques, namely Recurrent Neural Network (RNN) with Gated recurrent unit and Support Vector Machines (SVM). The dataset consists of opinions on effect of COVID-19 pandemic on air travel. The evaluation metrics used include precision, accuracy, recall and F1-score. The experimental analysis showed a significant increase from 9.28% under a standard sentiment review to 10.1% optimized sentiment analysis. The findings further show a significant improvement in the performance of optimized SVM yielding an improved prediction performance compared to RNN. The outcome of this study will support airlines to understand the frustration and complaints of customers and to make concrete decisions on how to improve their services. The framework will serve as a benchmark for future sentiment analysis in other sectors where customer views and comments are core to their services.

The mediator role of well-being in the effect of COVID-19 anxiety on occupational commitment: research in the aviation sector.

The coronavirus (COVID-19) pandemic, which emerged in China in December 2019, has severely affected many industries across the world and created substantial psychological, social and economic impact on individuals. With the coronavirus outbreak labelled as a pandemic by the World Health Organization, the first measures have been taken for the aviation industry. The crisis environment created by the pandemic had a negative impact on aviation personnel. The main purpose of this research is to investigate the mediator role of employee well-being in the effect of COVID-19 anxiety on occupational commitment. The data were collected through a survey of cabin and cockpit staff (n = 3862). After the analyses, it was found that the effect of COVID-19 anxiety on well-being, and occupational affective and normative commitment was significant. Moreover, it is among the findings that well-being has a partial mediator role in the effect of COVID-19 anxiety on occupational affective and normative commitment.

The Impact of COVID-19 on the Cybersecurity in Civil Aviation: Review and Analysis

The significance of cybersecurity and cyber resilience in the aviation sector cannot be ignored, and this fact has been highlighted time and again due to frequent cyberattacks prior and during the COVID-19 pandemic. This paper presents an analysis of the relevant studies that addressed the impact of the COVID-19 pandemic on cybersecurity generally, and in the aviation sector in particular, including identifying the most common increased cyber-attacks associated with the outbreak of COVID-19. Our analysis shows that commonly increased cyber-attacks associated with the outbreak of COVID-19 in aviation are phishing and ransomware. Furthermore, the likelihood of phishing and ransomware attacks is very likely during the pandemic than before it while the impact on flight operations is low, and the subsequent risk level is tolerable during the pandemic. Additionally, we propose a set of possible mitigation measures for those cyber-attacks. © 2022 IEEE.

Forecast of Aviation Traffic in Indonesia Based on Google Trend and Macroeconomic Data using Long Short-Term Memory

The COVID-19 pandemic has impacted many sectors. For example, in the aviation sector, flight traffic went down drastically with no certainty of being recovered. This calls for a methodology to predict the flight traffic to provide strategic planning on flight schedules operational, route structuring, and flight navigation service cost determination. However, current developments mainly focus on flight traffic forecasting based on historical data without considering external factors. In this study, we propose the Long Short-Term Memory (LSTM) technique to forecast flight traffic in Indonesia involving external variables such as macroeconomic variables and Google Trends. LSTM is proposed because of its flexibility to model non-linear time series data and has a good reputation for predictive accuracy. We first select a few among Google Trends and macroeconomic variables using nonlinearity analysis and cross-correlation function (CCF). We then employ the selected variables to forecast the flight traffic and compare it to the one using only historical flight traffic data. Our results concluded, based on the Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE), that the model involving google trend outperforms the other three models, i.e., the model with only historical data, the model with macroeconomics, and the model with both macroeconomic and Google Trends. It is because, in this digital era, Google Trends can reflect population psychology in an up-to-date manner. © 2022 IEEE.

Prediction of Flight Fare using Deep Learning Techniques

Due to the drastic growth in aviation sector, population living standard and adverse effect of the Covid-19 pandemic there is an increase in air travel in the recent years. However, Indian airline corporation use revenue management system to make real-time price adjustments, causing fare to fluctuate considerably. The price is characterized by considerable fluctuation, making it viable to conduct research on price prediction. This issue is addressed in this work where flight fare prediction is carried out by implementing deep neural network system. Furthermore, the attributes of the given dataset have been analyzed using various visualization technique such as correlation matrix, boxplot, bar chart and line plot. The results justify that the random forest and the gradient boos technique gives highest accuracy with the fare prediction dataset. © 2022 IEEE.

E-Commerce: Challenges That Lies Ahead of the Future Air Cargo Operation

Air cargo is growing exponentially and can become one of the main revenues for aviation sector. With the COVID-19 crisis, air cargo plays an important role for deliver much-needed products such as vaccine or medical supplies. Moreover, E-commerce is also affecting the growth of air cargo industry. The purpose of this study is to explore the current state of air cargo industry and explore the challenges that air cargo industry will meet after the pandemic ends. The result shows that capacity shortage is the most significant challenges that air cargo need to consider in order to cope with rapid demand. The second factor is speed which is also important since customers demanding for the fast delivery. © 2021 IEEE.

Public Financial Support to Aviation During the COVID-19 in the Baltic Countries

The paper analyses government financial support (state aid) arrangements to the aviation sector in the Baltic countries due to the COVID-19 pandemic situation. The support schemes include three main features. First, the design of public support schemes grounds on a standardized Keynesian framework of stimulation measures during economic recessions. Second, state aid is a rather sensitive issue in the European Union context. Support schemes are generally banned and strictly regulated even in the global pandemic situation. Third, stimulation measures depend on countries' capabilities to support their flag carriers. Which has a direct impact on the post-COVID-19 competition situation in the region. The paper focuses on comparative analyses of the scope of state aid in the COVID-19 situation in the Baltic Sea Region. In the frames of research is given an overview of stimulative instruments proposed by international aviation organizations. As a conclusion will be discussed motives to support aviation and stimulation schemes particularities. © 2021 Kaunas University of Technology. All rights reserved.

SITUATION OF EUROPEAN AIRLINES CAUSED BY COVID-19: RESTRICTIONS, GOVERNMENT SUBSIDIES AND FUTURE PROSPECTS

The COVID-19 pandemic has been an unprecedented crisis, severely affecting the economy and many sectors, including the airline industry. This paper reviews this situation to see how airlines have acted since the beginning of COVID-19. The airline industry is dependent on financial support and subsidies to cope with the massive drop in air travel due to the coronavirus. The support received by the major airlines has been examined. In addition, a comparison has been made of all the aviation restrictions that have been implemented by different European governments. Travellers from countries with a higher incidence of cases, or with a growing rate of cases, have the most restrictions on travel to other countries. Furthermore, the strategies and protocols being implemented by certain airlines following the lifting of some of the restrictions on passenger air traffic are analysed. 'Ibis paper will provide an insight into how airlines are coping with this unfavourable environment, as well as some of the future prospects and strategies of the aviation sector.