AirBERT: A fine-tuned language representation model for airlines tweet sentiment analysis
Intelligent Decision Technologies, ISSN: 1875-8843, Vol: 17, Issue: 2, Page: 435-455
2023
- 14Citations
- 30Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
Airlines operate in a competitive marketplace and must upgrade their services to meet customer safety and comfort. Post-pandemic, the government and airlines resumed flights with many restrictions, the impact which is unexplored. An increasing number of customers use social media to leave reviews and in this age of Machine Learning (ML), if a model is available to automatically polarize flyer sentiments, it can help airlines upscale. In this work, a custom dataset is scraped from Twitter by including online reviews of five Indian airlines. Multiclass sentiment analysis using three classifiers, support vector machine, K-nearest neighbor and random forest with word2vec and TF-IDF word embeddings is implemented. AirBERT, a fine-tuned deep learning attention model based on bidirectional encoder representation from transformers is proposed. From results, it is observed that on ML, Random Forest with TF-IDF performs the best but the graphical processing unit and domain corpora trained AirBERT outperforms all the other models with an accuracy of 91%. Indigo airlines and Jet Airways received the maximum percentage of positive and negative reviews respectively. In performance comparison with three existing models on the USA airlines tweets dataset, the proposed model outperforms others trained on general domain corpora and matches state-of-the-art TweetBERTv2 model accuracy. The model can be deployed by airlines and other service industries to implement a customer relationship management (CRM) system.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85156159402&origin=inward; http://dx.doi.org/10.3233/idt-220173; https://journals.sagepub.com/doi/full/10.3233/IDT-220173; https://dx.doi.org/10.3233/idt-220173; https://content.iospress.com:443/articles/intelligent-decision-technologies/idt220173
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