## 2 Related Works
### Lexicon-Based Approaches
[PERSON] and [PERSON] (2014) [37] proposed a valence-aware dictionary and sentiment reasoner (VADER), a lexicon- and rule-based sentiment analysis tool that is specifically attuned to sentiments expressed from online text collected from social media or the Internet. VADER (available online: [[http://www.nltk.org/_modules/nltk/sentiment/vader.html](http://www.nltk.org/_modules/nltk/sentiment/vader.html)]([http://www.nltk.org/_modules/nltk/sentiment/vader.html](http://www.nltk.org/_modules/nltk/sentiment/vader.html)) (accessed on 17 September 2024)) supports the handling of emoticons, idioms, punctuation, negation, emphasis, and contrasts. VADER also considers the impact of words in ALL CAPS to emphasise their meaning. Depending on the original sentiment of the word, the overall polarity is adjusted by 0.733. Additionally, VADER can identify negated sentences and evaluate sentiment shifts brought on by contrastive conjunctions like \"but\". However, the empirical validation of the VADER is based on multiple independent human judges that incorporate a \"goldstandard\" sentiment lexicon that is especially attuned to microblog-like contexts. [PERSON] et al. [38] used VADER to analyse and perform opinion mining on Twitter data. In addition to the binary classification system that almost all other Twitter-based sentiment analysis models provided, their model also provided a multi-classification system. It was observed that VADER was an apt selection to analyse large sets of data. This model's limitations were that a small percentage of the actual data were employed, a general lexicon was used to categorise the data, and the data were not trained. [PERSON] and [PERSON] [39] used VADER to analyse student evaluations of teaching from three sources. The positive/negative valences of the comments were compared, frequently used keywords in comments were identified, and the impact of those comments containing said keywords was determined. [PERSON] et al. [40] used VADER to analyse sentiments in social media posts using individual words and sentences. The text of tweets was preprocessed to remove unwanted characters and words such as punctuation, unicode problems, URLs, emails, currency symbols, and numerals. [PERSON] et al. [41] used natural language processing (NLP) with VADER to predict the sentiment from social media data such as X, Facebook, and Reddit, and the results are interpreted and explained using heatmaps. Using VADER to evaluate sentiments in the Twitter dataset, the author achieved 69.52% accuracy, 64.88% precision, 85.10% recall, and 73.63% F1-score for the tweet, respectively, after normalisation. In their study, [PERSON] et al. [42] utilised a linear SVM as a machine learning classification model and VADER to predict customer feedback sentiment for the Huge Swedish Telecom Corporation by analysing a dataset of 168,010 emails. The author employed the Swedish sentiment Lexicon and VADER for sentiment analysis, achieving an F1 score of 83.4% and a mean AUC of 0.896. Moreover, the author in [42] identified a pattern in email discussions that could potentially predict the emotions of unseen emails. [PERSON] et al. [43] proposed a method to summarise customer reviews of electronic products from Amazon and C net.com based on product features. Nevertheless, many incorrectly spelt words are part of the features that frequently appear in social media content, which can make it challenging for automated systems to accurately identify and summarise customer opinions. Additionally, the constant evolution of language and slang used in online reviews adds another layer of complexity to the task of extracting meaningful insights from customer feedback. [PERSON] et al. [44] proposed SentimentNet. 3.0, a lexical resource specifically designed to enable sentiment classification and opinion mining applications.