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

Abstract

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.