Significant Labels in Sentiment Analysis of Online Customer Reviews of Airlines

Ahmed, Ayat Zaki; Rodríguez‐Díaz, Manuel

doi:10.3390/su12208683

Cited by 20 publications

(5 citation statements)

References 91 publications

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“…This information can help them improve their marketing strategies, create targeted campaigns, and ultimately increase sales. For instance, a study conducted by Zaki Ahmed and Rodríguez-Díaz [160] used sentiment analysis to monitor the online reputation of various airlines. The study analyzed tweets containing airline names and categorized the sentiments as positive, negative, or neutral.…”

Section: Resources and Tools For Sentiment Analysismentioning

confidence: 99%

A Review of Deep Learning Models for Twitter Sentiment Analysis: Challenges and Opportunities

Chaudhary,

Girdhar,

Sharma

et al. 2024

IEEE Trans. Comput. Soc. Syst.

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Microblogging site Twitter is one of the most influential online social media websites, that offers a platform for the masses to communicate, express their opinions, and share information on a wide range of subjects and products, resulting in the creation of a large amount of unstructured data. This has attracted significant attention from researchers, who seek to understand and analyze the sentiments contained within this massive user-generated text. The task of sentiment analysis entails extracting and identifying user opinions from the text, and various lexicon and machine learning-based methods have been developed over the years to accomplish this. However, deep learning-based approaches have recently become dominant due to their superior performance. The current study briefs on standard preprocessing techniques and various word embeddings for data preparation. It then delves into a taxonomy to provide a comprehensive summary of deep learning-based approaches. Additionally, the work compiles popular benchmark datasets and highlights evaluation metrics employed for performance measures as well as the resources available in the public domain to aid sentiment analysis tasks. Furthermore, the survey discusses domain-specific practical applications of sentiment analysis tasks. Finally, the study concludes with various research challenges and outlines future outlooks for further investigation.

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Section: Resources and Tools For Sentiment Analysismentioning

confidence: 99%

A Review of Deep Learning Models for Twitter Sentiment Analysis: Challenges and Opportunities

Chaudhary,

Girdhar,

Sharma

et al. 2024

IEEE Trans. Comput. Soc. Syst.

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“…Zaki et al [ 39 ] proposed a methodology for determining the significant labels denoting customer sentiments. The titles of the comments, which typically include the terms that most effectively characterize the customer experience, were combed through to locate relevant labels.…”

Section: Literature Reviewmentioning

confidence: 99%

LSTM‐DGWO‐Based Sentiment Analysis Framework for Analyzing Online Customer Reviews

Barik¹,

Misra

Ray³

et al. 2023

Computational Intelligence and Neuroscience

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Sentiment analysis furnishes consumer concerns regarding products, enabling product enhancement development. Existing sentiment analysis using machine learning techniques is computationally intensive and less reliable. Deep learning in sentiment analysis approaches such as long short term memory has adequately evolved, and the selection of optimal hyperparameters is a significant issue. This study combines the LSTM with differential grey wolf optimization (LSTM-DGWO) deep learning model. The app review dataset is processed using the bidirectional encoder representations from transformers (BERT) framework for efficient word embeddings. Then, review features are extracted by the genetic algorithm (GA), and the optimal review feature set is extracted using the firefly algorithm (FA). Finally, the LSTM-DGWO model categorizes app reviews, and the DGWO algorithm optimizes the hyperparameters of the LSTM model. The proposed model outperformed conventional methods with a greater accuracy of 98.89%. The findings demonstrate that sentiment analysis can be practically applied to understand the customer’s perception of enhancing products from a business perspective.

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“…Sentiment analysis in the airline industry: Sentiment analysis has gained significant popularity in the airline industry as a means of understanding customer sentiment towards services and evaluating overall service quality (Kang et al, 2022;Kwon et al, 2021). Recent studies have examined sentiment in online reviews of airline services, establishing a strong link between customer sentiment, airline choice, and satisfaction (Ahmed & Rodríguez-Díaz, 2020;Lucini et al, 2019;Ractham, 2021). These findings highlight the importance of accurately assessing and comprehending customer sentiment in meeting expectations and improving perceptions of service quality.…”

Section: Literature Reviewmentioning

confidence: 99%

A Study on Sentiment Analysis on Airline Quality Services: A Conceptual Paper

Idris,

Mohamad

2023

IMBR

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Airline quality service is crucial for airlines to remain competitive in the industry. The quality of the services of these airlines must meet customer satisfaction and other aspects of the overall service experience. The levels of service quality in an airline service may impact satisfaction and loyalty which may influence customer sentiment. Concerning the importance of airline quality service, customer sentiment towards the service must be investigated and one of the ways to analyze it is by using sentiment analysis. Sentiment analysis is the chosen tool nowadays to analyze comments or reviews made on these services, which may be positive, negative, or neutral. Using sentiment analysis, will not only help potential customers to view the overall sentiment portrayed, but organizations can also use the findings to improve their organization to be more competitive. Thus, this paper will focus on reviewing several recent works related to sentiment analysis as a tool for assisting organizations in assessing the quality of services in the airline industry. As a result, a new framework for assessing the quality of service for the organizations, especially the airline company will be proposed.

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Significant Labels in Sentiment Analysis of Online Customer Reviews of Airlines

Cited by 20 publications

References 91 publications

A Review of Deep Learning Models for Twitter Sentiment Analysis: Challenges and Opportunities

A Review of Deep Learning Models for Twitter Sentiment Analysis: Challenges and Opportunities

LSTM‐DGWO‐Based Sentiment Analysis Framework for Analyzing Online Customer Reviews

A Study on Sentiment Analysis on Airline Quality Services: A Conceptual Paper

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