## 2 Related Work  
Sentiment analysis or opinion mining is the process of extracting people's opinions, emotions, attitudes, and feelings about a topic or situation from a large amount of unstructured data. A large body of research has been done in recent years to develop methods for analyzing and describing the process of sentiment analysis in different languages. The study [13] analyzed the emotions of educational tweets during COVID-19 on the dataset obtained using the NLP toolkit and naive-based classifier. Results show that the number of tweets with negative emotions has exceeded the number of tweets with positive emotions. Another study about online education is [14] where the dataset of 1717 tweets is collected for analysis. After cleaning, 1548 tweets are extracted and categorized as favorable, negative, or neutral with an accuracy of 74.9%. A total of 154 articles about online learning are retrieved from Google and other platforms including online reviews and blogging and sentiment analysis are performed through text mining using the dictionary-based technique of the lexicon-based approach in [15]. Polarity and subjectivity of articles are obtained using the TextBlob toolkit. Similarly, comments about online learning from learners, professionals, and guardians are gathered to assess educational system reforms in [16]. The study [17] compares the efficiency of the online education system with traditional classrooms with a focus on students enrolled in higher education. Research suggests that 73 percent of students have appropriate internet access and 71.4 percent of students feel well equipped to operate a computer/laptop for online classes. However, 78.6 percent of respondents believe that traditional classrooms are more effective than online learning. [PERSON] et al. [18] investigate sentiment analysis of tweets to grasp better understanding of people's sentiments and opinions about online education in the mid of COVID-19. The study performs experiments using NB (Naive Bayes), KNN (K-Nearest Neighbour), and RF (Random Forest) classifiers. In comparison to NB and KNN, the RF multi-class classification technique shows the best classification accuracy due to its ability to work well with high-dimensional data such as text categorization. [PERSON] et al. [19] proposed a model that accurately classifies the sentiments into positive, negative, and neutral. Furthermore, three basic approaches are used for sentiment analysis. First, a lexicon-based approach is used in which the sentiment lexicon is to describe the polarity and subjectivity score of textual data into positive, negative, and neutral. Machine learning algorithmsare easy to implement and understand but require human efforts for labeling. Secondly, the machine learning-based approach requires labeled data to train the classifier manually for better performance. Three, a hybrid approach is a combination of machine learning and a lexicon-based approach. The authors in [20] analyze movie reviews using KNN, NB, and LR (Logistic Regression). The dataset is gathered from several sources for analysis, and LR provides the highest accuracy. In both short and lengthy text content, many classifiers are tested. For brief text, NB and LR produce average outcomes of 91 and 74 percent, respectively. Both models do poorly on long texts [21]. Machine Learning models produce good results when it comes to categorizing product reviews. For camera reviews, NB has an accuracy of 98.17 percent and SVM (Support Vector Machine) an accuracy of 93.54 percent [22]. Furthermore, according to [23], sentiment analysis is the analysis of opinions involving NLP, computer science, theory of computation, and artificial intelligence. Subjectivity and polarity are two components of sentiment analysis. Polarity expresses emotions that can be positive or negative scores while subjectivity identifies the attitudes, feelings, and opinions [24]. Another study [25], performs sentiment analysis on COVID-19 tweets using machine learning and lexicon-based techniques.