*2.2. Sentiment Data Analysis—Machine Learning Approach (MLP)*

Primary sentiment analysis was conducted on the dataset using Azure on Microsoft Excel. The software yielded the results as 'positive', 'negative' or 'neutral' and scored the confidence of the analysis, with a score of 1 being most confident with the analysis and 0 being least confident.

#### *2.3. Sentiment Data Analysis—Lexicon-Based Approach*

A Python-based API for Twitter was used to collect live tweets, which were recorded into a relational database using SQLite. Sentiment analysis was performed post-collection using the VADER algorithm, as part of the NLTK Python package. It is worth noting that Python version 3.9.0 was used throughout this process. Custom-made software built with Python 3.9.0 was used to perform the word frequency analysis. NLTK was used in the pre-processing of tweets—to remove stop words—prior to the word frequency analysis.

The provided sentiment compound—or sentiment score—calculated from the sum of lexicon ratings, was normalised between −1 (extreme negative) and +1 (extreme positive). This technique determined the polarity—or positivity and negativity—and the intensity of the expressed emotion. The intensity of emotion of each tweet is divided into the quantity of positive, negative and neutral elements the tweet contained—adding to a total value of 1. Each tweet was classified as positive, negative or neutral according to its compound score. Compound scores less than 0.05 were considered negative, scores between −0.05 and 0.05 were considered neutral and scores above 0.05 were classified as positive [41,55].
