## 2 Related Works
### Lexicon-Based Approaches  
Lexicon-based approaches are popular for detecting sentiment in text because they are simpler and faster than supervised learning methods. [PERSON] et al. [31] conducted a study comparing the performance of supervised and unsupervised machine learning techniques for sentiment analysis from a given set of Twitter messages. Their findings revealed that supervised methods generally exhibit superior accuracy to unsupervised approaches like lexicon-based algorithms. Nonetheless, acquiring sufficient labelled training data for supervised methods can be costly and time-consuming. [PERSON] et al. [32] used a lexicon to find (a) the mood of short, casual text messages like tweets and SMS on the SemEval-2013 dataset (message-level task) and (b) the mood of a word or phrase within a message (term-level task). On top of that, the researcher employs commonly used statistical-style features such as word, character, elongated, punctuation, and POS tag counts, as well as common Twitter-specific attributes such as emoticons and hashtag counts developed to handle negation. The scheme is tested by selecting 1,455 high-frequency terms from the Sentiment140 Corpus and the Hashtag SentimentCorpus, which includes 1.6 million tweets with positive and negative sentiment labels. The data consist of regular English words, Twitter-specific terms (e.g., emoticons, abbreviations, and creative spellings), and negated natural expressions manually annotated using MaxDiff (available online: [[https://saifmohammad.com/WebPages/lexicons.html](https://saifmohammad.com/WebPages/lexicons.html)]([https://saifmohammad.com/WebPages/lexicons.html](https://saifmohammad.com/WebPages/lexicons.html)) (accessed on 1 October 2024)) to assign a score to the most prominent words in the individual tweet and the SMS [33]. The approach achieved a macro-averaged F-score of 69.02% in the message-level task and 88.93% in the term-level assignment. A linear-kernel support vector machine (SVM) was used to train the SMS messages. The system achieved an F-score of 70.45% for the message-level task and an F-score of 89.50% for the term-level task. It came in second for detecting the sentiment of terms within SMS messages (F-score of 88.00-, 0.39 points behind the first-ranked system). [PERSON], and [PERSON] [34] introduced affective norms for English words (ANEWs), which provide emotional assessments for many English words. The positivity and intensity levels of stimuli impact emotional responses, influencing how we process and perceive emotional information. The author of [35] presented AFINN-96, which consists of 2477 distinct words, with an addition of 15 phrases that were not used. The author simplified the process by focusing only on valence when scoring the words, excluding factors like subjectivity/objectivity, arousal, and dominance, and assigning scores manually. The author of [35] used ANEW as a classification-based fuzzy model as a basis for expressing stochastic with five label terms for classifying tweets into five fuzzy opinion categories, very negative, negative, neutral, positive, and very positive, which allowed for a more nuanced understanding of sentiment in social media data. While ANEW includes many words commonly used in English, it lacks depth due to the evolving nature of language use in online communication and social media posts. ANEW does not account for the use of negations in words, making evaluation challenging as negations do not always reverse the meaning of each word, especially when adverbs are involved, leading to increased complexity. The author of [36] introduced a novel approach for analysing the sentiments from tweets that include a mix of adjectives, adverbs, and verbs to determine the sentiment score, and the actual polarity of the text or online communication is classified using a linear function that incorporates emotion intensity.