## 2 Sentiment Analysis Algorithms
### Deep Learning Approach
The proposed model was evaluated on two benchmark datasets related to the COVID-19 vaccine and Indian farmer protests, achieving testing accuracies of 91% and 81.49%, respectively. A two-stage emotion detection methodology for sentiment analysis was introduced by [PERSON] et al. (2022) [49]. The first stage was an unsupervised zero-shot learning model based on a sentence transformer that returned probabilities for 34 emotions. The second stage trained a machine learning classifier on the sentiment labels using ensemble learning. The proposed hybrid semi-supervised method achieved the highest accuracy of 87.3% on the English SemEval 2017 dataset. The method was evaluated on three benchmark datasets using multiple classifiers, including machine learning, neural network, and ensemble learning. The best accuracy achieved was 0.87 for the binary sentiment classification problem and 0.63 for the three-class sentiment classification problem, with the sets of 10 and 6 emotions, respectively. In the study by [PERSON] et al. (2022) [50], the researchers used public content from Twitter to analyze insomnia-related Tweets based on time intervals. They performed sentiment analysis using pretrained transformers and the Dempster-Shafer theory (DST) to classify emotions as positive, negative, or neutral. They validated their pipeline on 300 annotated Tweets and achieved 84% accuracy. Using logistic regression, they found that the odds of posting negative Tweets about insomnia were higher in the peripandemic interval than in the prepandemic interval. [PERSON] and [PERSON] (2022) [51] proposed a light-transformer model for sentiment analysis, which combined word-vector representation and positional embedding to extract sentence features. The model was based on the transformer architecture but only used the encodermodule and simplified the number of layers and parameters. The proposed method showed a 0.3-1.0% improvement in classification accuracy compared to traditional methods such as LSTM and CNN while greatly reducing the number of model parameters. The method yielded an accuracy of 76.40% on the NLPCC2014 Task2 dataset. In recent years, deep learning has become a popular method for sentiment analysis. Various models, such as CNNs, RNNs, LSTM, and GRUs have been used for representation learning and classification. Pretrained embeddings such as GloVe and word2 vec have been used to encode the preprocessed textual data. In several studies, MLP was used for sentiment analysis on different datasets and languages, including Korean, English, Turkish, COVID-19-related, and Bangla Tweets. The accuracies of the MLP model ranged from 75.03% to 93.73%. Other methods, such as naive Bayes, support vector machine, logistic regression, k-nearest neighbors, and an ensemble model were also tested in some studies, with the best results achieved with a CNN with 99.33% accuracy. In addition, LSTM and Bi-LSTM were used for sentiment analysis on different datasets, including the Saudi dialect and English. The Bi-LSTM model achieved the highest accuracy of 94% in one study. Table 2 provides a summary of the deep learning approaches.