


Abtract of the paper:
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.



We perform our experiment on a Personal Computer (PC) with Intel(R) Core(TM) i7-9700 CPU with a processor speed of 3.00 GHz and 32 GHz RAM. Python Programming Language implemented in Jupyter Notebook is used for these experiments. 
We utilised libraires such as NLTK, Scikit-Learn for the analysis, and Beautiful Soup library for scraping the reviews.


## Contents of the repository
This repository contains resources used and described in the paper.

The repository is structured as follows:
> **NOTE:** this should follow the folders you have in this repository

code: for the employed five classifires.
data: the pre-processed dataset for the experiments.

## Code
We applied these five methods in order to compare between them in the domain of Airport Service Quality (ASQ).

## Data
Dataset used for this paper, (processed_numerical_columns) is for the used five classifiers. Also, the review text column has been converted into nummerical values inside the code by using TF-IDF.
This dataset has been scrapped from SKYTRAX website "https://www.airlinequality.com/review-pages/a-z-airport-reviews/". 
