Repository for "Automatic Sentiment Lexicon Creation for Airport Services Reviews Using Pointwise Mutual Information"

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.