Mobile Technology Application in Aviation: Chatbot for Airline Customer Experience

Sarol, Sufi Dzikri; Mohammad, Mohammad Fakhrulnizam; Rahman, Nor Aida Abdul

doi:10.1007/978-981-19-6619-4_5

Cited by 5 publications

(2 citation statements)

References 18 publications

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“…Using the models trained by BERT and open AI-GPT, customers receive intelligent and personalized messages for their complaints and queries. Even if the customer tweet is not in a particular format and provides more information than necessary, the model can intelligently correlate it with the information it has been trained on and gives a natural language response (Sarol et al, 2022). To the customer, it seems like they are interacting with customer service personnel than a bot.…”

Section: Bert Vs Canned Responsesmentioning

confidence: 99%

Analyzing and Automating Customer Service Queries on Twitter Using Robotic Process Automation

Kumar¹,

Shidaganti²,

Anand³

et al. 2023

Journal of Computer Science

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Several product companies have turned to social media to analyze customer satisfaction and provide customer service to disgruntled customers. Product twitter handles are flooded with tweets every day. Customer service professionals struggle to find and resolve complaints from numerous tweets, which results in high wait times for a response, huge costs for the company, and frustrated customers. Automating customer services involves the use of Artificial Intelligence (AI) and Natural Language Processing (NLP) to emulate customer service offered by professionals. Pre-processing and deriving insights from real data are difficult and several start-ups cannot afford funds to maintain a data analysis team. Existing technologies for chatbots and analysis can be improved with new machine learning models, training existing models more and ensuring that responses generated resemble human interactions. BERT-CNN-BiLSTM modules were integrated into a model for sentiment analysis on scraped tweets from twitter using TWINT that achieved an accuracy of 96%. For the labeling and categorization of tweets, the logistic regression linear model achieved the highest accuracy of 97% compared to the other 3 classification models. For the automated chatbot, a model trained with BERT and open AI-GPT achieved an accuracy of 78%.

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Section: Bert Vs Canned Responsesmentioning

confidence: 99%

Analyzing and Automating Customer Service Queries on Twitter Using Robotic Process Automation

Kumar¹,

Shidaganti²,

Anand³

et al. 2023

Journal of Computer Science

View full text Add to dashboard Cite

show abstract

“…• Mobile computing based applications: Mobile based applications provide more soothing environment to the passengers. Mobile technology can contribute in different ways constituting passenger registration and booking flight over online platforms, tracking the delivery goods, looking at flight schedules and mandatory updates (Sarol et al, 2022).…”

Section: Various Trending Technologiesmentioning

confidence: 99%

Trending Digital Innovations in Aerospace and Aviation Industry

2023

IJFMR

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The Influence of Chatbots Adoption on Customer Loyalty in the Foodservice Sector

Rosman,

Putranto,

Yuliana

2023

2023 5th International Conference on Cybernetics and Intelligent System (ICORIS)

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Mobile Technology Application in Aviation: Chatbot for Airline Customer Experience

Cited by 5 publications

References 18 publications

Analyzing and Automating Customer Service Queries on Twitter Using Robotic Process Automation

Analyzing and Automating Customer Service Queries on Twitter Using Robotic Process Automation

Trending Digital Innovations in Aerospace and Aviation Industry

The Influence of Chatbots Adoption on Customer Loyalty in the Foodservice Sector

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