## 2 Related Works
### Lexicon-Based Approaches
SentimentNet 3.0 is an upgraded version of SentiWordNet 1.0, a lexical resource made publicly available for research purposes licensed to over 300 research groups and used in a wide range of research projects throughout the world [44]. SentiWordNet 1.0 and 3.0 are the outcomes of automatically annotating all WordNet synsets for positivity, negativity, and neutrality. [PERSON] et al. [45] used fuzzy ontology subgraphs based on lexical dictionaries to find morphological features in VKontakte text fragments, such as word, smile, or style. They determined sentiment by analysing features of the subject area, focusing on syntagmatic units rather than individual words for compatibility. The lexical ontology was assessed using SentiWordNet 3.0, which is built on WordNet 3.0. The method was tested using ML algorithms such as the Naive Bayes (NB) classifier, linear regression, and SVM on 420 VKontakte posts and comments. The average accuracy achieved was 78.33%, 65.24%, and 75.25%, respectively. The study revealed that the NB classifier performed better than the other ML algorithms tested. [PERSON] et al. [46] retrieved a dataset from an official product review site. The data were cleaned by eliminating unnecessary elements like stop words, verbs, punctuation, and conjunctions. In the referenced study, the author computes the positive and negative probabilities for each word in the dataset, merges these probabilities, and determines the sentiment based on the higher probability. To determine sentiment, each set of data is converted into a more complex format, and then a mathematical operation is used to identify the strongest indicator of sentiment, with the assistance of SentiWordNet. The dataset is trained using NB, SVM, and Ensemble methods with positive and negative labels, resulting in an accuracy of 78.86%. Nevertheless, the accuracy of the predictions fluctuates depending on the number of classifiers combined for the review output. It is also difficult to precisely interpret how users convey their emotions through emoticons in the reviews. Selecting an appropriate feature subset is crucial in sentiment classification. Tools like LIWC can extract psycholinguistic aspects from texts for analysis [47]. For example, [PERSON] et al. [48] introduced a psycholinguistic approach to sentiment analysis on Twitter. LIWC extracts psycholinguistic features like linguistic processes, psychological aspects, personal concerns, spoken categories, and punctuation from texts. These features are then processed using an ML algorithm. The author in [48] tested the proposed approach on English Twitter messages, including 6218 negative, 4891 positive, and 4252 neutral tweets. NB has the highest predicted performance (77.35% accuracy) when using the linguistic feature set. Incorporating ensembles, the Random Subspace Ensemble of NB achieves a classification accuracy of 89.10%. In the same way, [PERSON] et al. [49] used five main personality traits and 52 linguistic categories to find links between things like self-reports, reports from others, life outcomes, and behavioural measures of personality for text-based assessments. The results indicate that text-based personality assessment offers precise and dependable insights into an individual's personality traits. As another example, [PERSON] et al. [50] used the computerised LIWC to quantify students' cognitive, emotional, and social engagement in social annotation. Additionally, the author in [50] explored how students with varying levels of engagement differ in their social annotation behaviours. They used a statistical method to analyse data from 91 undergraduate students and 29 reading materials, successfully identifying two different engagement patterns with a high Cohen's Kappa of 0.78. The two different engagement patterns were labelled as \"active\" and \"passive\".