


Abtract of the paper:
Abstract—Airport service quality is considered to be an indicator of passenger satisfaction. However, assessing this by conventional methods requires continuous observation and monitoring.
Therefore, during the past few years, the use of machine learning techniques for this purpose has attracted considerable attention for analysing the sentiment of the air traveller.
A sentiment analysis system for textual data analytics leverages the natural language processing and machine learning techniques in order to determine whether a piece of writing is positive, negative or neutral.
Numerous methods exist for estimating sentiments which include lexical-based methodologies and directed artificial intelligence strategies. 
Despite the wide use and ubiquity of certain strategies, it remains unclear which is the best strategy for recognising the intensity of the sentiments of a message.
It is necessary to compare these techniques in order to understand their advantages, disadvantages and limitations.
In this paper, we compared the Valence Aware Dictionary and sentiment Reasoner, a sentiment analysis technique specifically attuned and well known for performing good on social media data, with the conventional
machine learning techniques of handling the textual data by converting it into numerical form. We used the review data obtained from the SKYTRAX website for each airport.
The machine learning algorithms evaluated in this paper are VADER sentiment and logistic regression. The term frequency-inverse document frequency is used in order to convert the textual review data into the resulting numerical columns.
This was formulated as a classification problem, whereby the prediction of the algorithm was compared with the actual recommendation of the passenger in the dataset. 
The results were analysed according to the accuracy, precision, recall and F1-score. From the analysis of the results, we observed that logistic regression outperformed the VADER sentiment analysis.



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 Vader sentiemnt analysis & logistic regression.
data: For VADER, For logistic regression & sentiment_scores

## Code
There are two files for the codes, the first one for the vader sentiment analysis. The second one for the logistic regression analysis. We applied these two methods in order to compare between them in the domain of Airport Service Quality (ASQ).

## Data
Dataset used for this paper, includes two versions: version 1 (skytrax_reviews) is for the vader sentiment analysis where there is only text analysis. Version 2 (processed_numerical_columns) for the logistic regression analysis after converting the text reveiw column into numerical values. Also,sentiment scores file is used for the analysis of Vader where vader scores have been extracted.
This dataset has been scrapped from SKYTRAX website "https://www.airlinequality.com/review-pages/a-z-airport-reviews/". 
