A sentiment analysis framework to classify instances of sarcastic sentiments within the aviation sector
Date
2023
Journal Title
Journal ISSN
Volume Title
Publisher
International Journal of Information Management Data Insights
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.
Description
Research Article
Keywords
Sentiment analysis, Social media, Aviation sector