Sentiment Analysis for Airline Tweets Utilizing Machine Learning Techniques

Kumar, G. Ravi; Sheshanna, K. Venkata; Babu, G. Suresh

doi:10.1007/978-3-030-49795-8_75

Cited by 6 publications

(2 citation statements)

References 11 publications

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“…Kumar et al additionally proposed a more advanced technique using artificial neural networks (ANNs) using the same dataset from Kaggle and compared the results with classifiers such as SVM and decision trees (DTs). They concluded that ANN performed better than DTs and slightly better than SVM when comparing their accuracy, precision, recall, and F1-scores ( 6 ).…”

Section: Literature Reviewmentioning

confidence: 99%

Machine Learning Approach to Analyze the Sentiment of Airline Passengers’ Tweets

Gao

2023

Transportation Research Record: Journal of the Transportation R

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As one of the most extensive social networking services, Twitter has more than 300 million active users as of 2022. Among its many functions, Twitter is now one of the go-to platforms for consumers to share their opinions about products or experiences, including flight services provided by commercial airlines. Using a machine learning approach, this study aimed to measure customer satisfaction by analyzing sentiments of tweets that mention airlines. Relevant tweets were retrieved from Twitter’s application programming interface and processed through tokenization and vectorization. After that, these processed vectors were passed into a pretrained machine learning classifier to predict the sentiments. In addition to sentiment analysis, we also performed a lexical analysis on the collected tweets to model keyword frequencies, which provided meaningful context to facilitate interpretation of the sentiments. We then applied time series methods such as Bollinger Bands to detect abnormalities in the sentiment data. Using historical records from January to July 2022, our approach was proven capable of capturing sudden and significant changes in passenger sentiments through the analysis of breakout points on the Bollinger upper and lower bounds. The methodology devised for this study has the potential to be developed into an application that could help airlines, along with other customer-facing businesses, efficiently detect abrupt changes in customer sentiments and consequently take appropriate mitigatory measures.

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Section: Literature Reviewmentioning

confidence: 99%

Machine Learning Approach to Analyze the Sentiment of Airline Passengers’ Tweets

Gao

2023

Transportation Research Record: Journal of the Transportation R

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“…However, the analysis of vast amounts of existing contextual data, coupled with continual daily updates, presents a labor-intensive challenge [4]. The rapid advancements in machine learning technologies, particularly in natural language processing (NLP), have significantly facilitated the big analysis of data in recent years [5][6][7]. Various studies have shown the effectiveness of large language models (LLMs) such as BERT (bidirectional encoder representations from transformers) and GPT (generative pre-trained transformers) in analyzing and classifying the sentimental contextual information of the text [8,9].…”

Section: Introductionmentioning

confidence: 99%

A Comparative Sentiment Analysis of Airline Customer Reviews Using Bidirectional Encoder Representations from Transformers (BERT) and Its Variants

Li,

Yang,

Huang

2023

Mathematics

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The applications of artificial intelligence (AI) and natural language processing (NLP) have significantly empowered the safety and operational efficiency within the aviation sector for safer and more efficient operations. Airlines derive informed decisions to enhance operational efficiency and strategic planning through extensive contextual analysis of customer reviews and feedback from social media, such as Twitter and Facebook. However, this form of analytical endeavor is labor-intensive and time-consuming. Extensive studies have investigated NLP algorithms for sentiment analysis based on textual customer feedback, thereby underscoring the necessity for an in-depth investigation of transformer architecture-based NLP models. In this study, we conducted an exploration of the large language model BERT and three of its derivatives using an airline sentiment tweet dataset for downstream tasks. We further honed this fine-tuning by adjusting the hyperparameters, thus improving the model’s consistency and precision of outcomes. With RoBERTa distinctly emerging as the most precise and overall effective model in both the binary (96.97%) and tri-class (86.89%) sentiment classification tasks and persisting in outperforming others in the balanced dataset for tri-class sentiment classification, our results validate the BERT models’ application in analyzing airline industry customer sentiment. In addition, this study identifies the scope for improvement in future studies, such as investigating more systematic and balanced datasets, applying other large language models, and using novel fine-tuning approaches. Our study serves as a pivotal benchmark for future exploration in customer sentiment analysis, with implications that extend from the airline industry to broader transportation sectors, where customer feedback plays a crucial role.

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A Comparative Study of Sentiment Analysis Using NLP and Different Machine Learning Techniques on US Airline Twitter Data

Tusar¹,

Islam²

2021

2021 International Conference on Electronics, Communications and Information Technology (ICECIT)

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Sentiment Analysis for Airline Tweets Utilizing Machine Learning Techniques

Cited by 6 publications

References 11 publications

Machine Learning Approach to Analyze the Sentiment of Airline Passengers’ Tweets

Machine Learning Approach to Analyze the Sentiment of Airline Passengers’ Tweets

A Comparative Sentiment Analysis of Airline Customer Reviews Using Bidirectional Encoder Representations from Transformers (BERT) and Its Variants

A Comparative Study of Sentiment Analysis Using NLP and Different Machine Learning Techniques on US Airline Twitter Data

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