Data supporting: 'Analysing the Sentiment of Air-Traveller: A Comparative Analysis'

Simple item page
dc.contributor.authorSalih A Homaid, Mohammed
dc.contributor.authorBala Bisandu, Desmond
dc.contributor.authorMoulitsas, Irene
dc.contributor.authorJenkins, Karl
dc.date.accessioned2024-06-03T06:45:43Z
dc.date.available2024-06-03T06:45:43Z
dc.date.issued2022-08-31 12:55
dc.description.abstractAirport service qualityis considered to be an indicator of passenger satisfaction. However, assessingthis by conventional methods requires continuous observation and monitoring.Therefore, during the past few years, the use of machine learning techniquesfor this purpose has attracted considerable attention for analysing thesentiment of the air traveller. A sentiment analysis system for textual dataanalytics leverages the natural language processing and machine learningtechniques in order to determine whether a piece of writing is positive, negativeor neutral. Numerous methods exist for estimating sentiments which includelexical-based methodologies and directed artificial intelligence strategies.Despite the wide use and ubiquity of certain strategies, it remains unclearwhich is the best strategy for recognising the intensity of the sentiments of amessage. It is necessary to compare these techniques in order to understandtheir advantages, disadvantages and limitations. In this paper, we compared theValence Aware Dictionary and sentiment Reasoner, a sentiment analysis techniquespecifically attuned and well known for performing good on social media data,with the conventional machine learning techniques of handling the textual databy converting it into numerical form. We used the review data obtained from theSKYTRAX website for each airport. The machine learning algorithms evaluated inthis paper are VADER sentiment and logistic regression. The termfrequency-inverse document frequency is used in order to convert the textualreview datainto the resulting numerical columns. This was formulated as a classificationproblem, whereby the prediction of the algorithm was compared with the actualrecommendation of the passenger in the dataset. The results were analysedaccording to the accuracy, precision, recall and F1-score. From the analysis ofthe results, we observed that logistic regression outperformed the VADERsentiment analysis.
dc.identifier.citationHomaid, Mohammed Salih A; Bisandu, Desmond Bala; Moulitsas, Irene; Jenkins, Karl (2022). Data supporting: 'Analysing the Sentiment of Air-Traveller: A Comparative Analysis'. Cranfield Online Research Data (CORD). Dataset. https://doi.org/10.17862/cranfield.rd.19375334
dc.identifier.doi10.17862/cranfield.rd.19375334
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/21764
dc.identifier.urihttps://doi.org/10.17862/cranfield.rd.19375334
dc.publisherCranfield University
dc.relation.isreferencedbyhttps://dspace.lib.cranfield.ac.uk/handle/1826/17812
dc.rightsMIT
dc.rights.urihttps://opensource.org/licenses/MIT
dc.subjectAirport service quality
dc.subjectdata analytics
dc.subjectmachine learning
dc.subjectsentiment analysis
dc.subjecttext mining
dc.subjectregression'
dc.titleData supporting: 'Analysing the Sentiment of Air-Traveller: A Comparative Analysis'
dc.typeDataset

Files

Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
Repository for 1309-A1002.zip
Size:
35.06 MB
Format:
Unknown data format
Download

Collections

Aerospace