## 2 Related Works
### Lexicon-Based Approaches
Despite a comprehensive examination of student interactions in social annotation, the study did not examine the interactions between students and instructors in social annotation or the contribution of the student-instructor relationships to assist in the overall learning outcomes. [PERSON] et al. [51] explored the patterns in how students used annotations and how they responded to them in social annotation activities. They examined how students' performance in behavioural, cognitive, emotional, and social areas changed based on their interactions. They gathered 93 undergraduates who were enrolled in an elective course at a large North American university, and the students were tasked with collaboratively annotating the class readings uploaded to Perusall, a social annotation platform, over 7 weeks. To determine exactly how to effectively classify student behaviours into groups, the researcher in [51] used metaclustering analysis based on the number of annotations and response behaviours. For example, they combined multiple clustering solutions to make them more robust and reliable. We looked at the number of annotation and response behaviours and used the K-means algorithm to find the best number of clusters from 905 data instances. Then, we used LIWC [52], a text mining tool to evaluate the levels of students' cognitive activities, specifically focusing on cognitive insight and cognitive discrepancy. Word Sense Disambiguation (WSD) is a method used to determine the meaning of a word with multiple senses. [PERSON] et al. [53] introduced the use of WSD to assign polarity based on an n-gram graph for analysing sentiment in text related to figurative phrases. This polarity assignment involves using a tool that adjusts the sentiment of figurative phrases in text or online content based on their context. It uses a range of eight surrounding words to determine similarity and generate a gloss vector (GV) in WSD. GV creates a co-occurrence matrix of words, where each cell in the matrix indicates the number of times the words represented by the row and the column occur together in a WordNet gloss. Each word in a WordNet gloss is depicted as a vector in a multi-dimensional space based on its specific row. For each word in the gloss, a context vector is formed by using the respective row in a matrix that shows how often words appear together. Subsequently, a gloss vector is generated as the average of all these context vectors for each word sense. The similarity is determined by analysing the parts of speech (POS) using a tool called the Stanford POS tagger. The next step involves assessing the sentiment by associating WordNet senses with words in the text, which are classified into positive or negative categories. The author used hidden Markov models (HMMs) to figure out how sentences made the author feel, and the results were confirmed in the Affective Text task of SemEval'07 [54]. The results show that this method effectively assigns sentiment to figurative language, indicating its potential for use in different NLP tasks. [PERSON] et al. [55] used WSD and SVM classification on non-English text to find emotions or people's feelings. They obtained a 91.8% per cent success rate, but this rate can go up if semantic and syntactic features are added. [PERSON] et al. [56] used historical stock prices of NASDAQ-100 companies and news articles from Dow Jones Newswires to test graph-based WSD as a sentiment predictor of stock price. They obtained a 53.3% success rate. However, the system cannot evaluate more complex trading strategies, and future research is expected to incorporate a more explicit notion of human sentiment with respect to news articles. Table 1 provides an overview of various lexicon-based methods for sentiment analysis. It shows the reference, the wording size, the attributes used to represent text, an acronym for the proposed model, the source or dataset, and the evaluation metric such as precision (PR), recall (RC), F1-score (F1), and accuracy (ACC) or result obtained for comparison.