Browsing by Author "Salih A Homaid, Mohammed"

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    Data supporting "Analysing the Sentiment of Air-Traveller: A Comparative Analysis"
    (Cranfield University, 2022-08-31 12:55) Salih A Homaid, Mohammed; Bala Bisandu, Desmond; Moulitsas, Irene; Jenkins, Karl
    Airport 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.
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    ItemOpen Access
    Repository for "Automatic Sentiment Lexicon Creation for Airport Services Reviews Using Pointwise Mutual Information"
    (Cranfield University, 2023-12-11 16:09) Salih A Homaid, Mohammed; Moulitsas, Irene; Chandrakumar, Mathura
    In this study, we propose a novel method to generate domain-specific sentiment lexicons for airport service reviews utilising the VADER sentiment lexicon dictionary. First, we scraped the data from the SKYTRAX website, which is a collection of reviews of around 600 airports. Then, data pre-processing techniques were employed including unigrams extraction and stopwords removal. Having done that, we employed pointwise mutual information to calculate the scores of the extracted unigrams. Then, we updated the default scores of VADER with the pointwise mutual information scores. We evaluated our results using the performance measures of accuracy, precision, recall, and F1-score. Two popular general sentiment lexicons are used as benchmarks. The results showed that our proposed lexicon dictionary for the domain of airport reviews outperformed the benchmarks with consistent considerable improvements achieving around 10% in accuracy and around 7% in F1-score.
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    ItemOpen Access
    Supporting data for "Measuring Airport Service Quality Using Machine Learning Algorithms"
    (Cranfield University, 2022-06-28 15:11) Salih A Homaid, Mohammed; Moulitsas, Irene
    The airport industry is a highly competitive market that has expanded quickly during the last two decades. Airport management usually measures the level of passenger satisfaction by applying the traditional methods, such as user surveys and expert opinions, which require time and effort to analyse. Recently, there has been considerable attention on employing machine learning techniques and sentiment analysis for measuring the level of passenger satisfaction. Sentiment analysis can be implemented using a range of different methods. However, it is still uncertain which techniques are better suited for recognising the sentiment for a particular subject domain or dataset. In this paper, we analyse the sentiment of air travellers using five different algorithms, namely Logistic Regression, XGBoost, Support Vector Machine, Random Forest and Naïve Bayes. We obtain our data set through the SKYTRAX website which is a collection of reviews of around 600 airports. We apply some pre-processing steps, such as converting the textual reviews into numerical form, by using the term frequency-inverse document frequency. We also remove stopwords from the text using the NLTK list of stopwords. We evaluate our results using the accuracy, precision, recall and F1_score performance metrics. Our analysis shows that XGBoost provides the most accurate results when compared with other algorithms.