## 2 Sentiment Analysis Mechanisms
### Machine Learning-Based Approach
The authors created three lexicons for storing depression terms and counted the number of tweets for each symptom. Different machine-learning algorithms were deployed to measure the accuracy of the results. Another interactive method for classifying sentiments is shown in [40], where teaching-learning-based optimizers were applied to a Twitter dataset. Different preprocessing steps were executed to remove stop words and symbols before applying four text-processing models. A feature selection algorithm was proposed to classify polarity features into positive and negative polarities, and finally, the results of the models were listed. As proposed in [41], machine-learning-based algorithms have been applied to different polarity languages. The dataset was collected from Twitter, where each tweet was converted and counted to an integer. The integer was converted to its TF-IDF score value, and finally, the score was applied to ML classifiers for prediction. The authors of [42] proposed another aspect-based sentiment analysis methodology by providing an automatic annotation of datasets from YouTube songs. These songs were extracted and applied based on the number of views and reviews and then an aspect filtration was provided based on five aspect categories. The overall reviews were optimized again based on the predefined aspects to determine the most important aspect category. Sentiment analysis of users' orientations is also conducted in health sectors to improve the users' feedback and provide deep insight into the provided services. As presented in [43], a sentiment analysis tool was provided for measuring people's attitudes in smart cities during the COVID-19 epidemic. The dataset was tested five times to measure the average accuracy of the classified instances. As shown in [44], sentiment analysis of user polarities based on natural language processing (NLP) was proposed to explore the attitudes of Gulf countries during COVID-19. The goal of the paper was to check whether there were mixed emotions among people or not. The dataset was extracted from Twitter API, and the polarities were classified in different countries. The authors stated that most Gulf people's attitude toward the epidemic was neutral. In addition to previous research, the authors of [45] provided an approach based on semi-supervised machine learning that measured the analyses of datasets collected from different social networks about several epidemics. The first analysis clustered the data from Word-2-Vec and Fast-Text and then explored the sentiment orientation based on the applied classification algorithms. The authors of [46] measured the sentiment analysis of public health but from the financial aspect. The dataset was collected from financial news and then the reviews were grouped based on four polarity attributes to view the overall orientation. Furthermore, different classification algorithms were applied to learn from the polarity attributes and explore sentiment accuracy. In addition to lexicon-based and ML-based methodologies, different deep learning and transfer learning methods can be applied to Arabic sentiment analysis. As presented in [47], a deep learning model for multitasking was applied for classifying Arabic sentiments. The research aimed to enhance the performance as the Arabic language has low resources and contains high morphological and linguistic terms. The authors proposed a long short-term memory (LSTM) deep learning model that explored the relationship between three and five sentiment polarities into a private layer. This layer contained an encoder for words to add flexibility to the features. The authors of [48] proposed a deep transfer-learning model for manipulating Arabic text. The Convolutional Neural Network (CNN) training was conducted to classify the sentiments based on a pre-annotated dataset. The authors collected different classes of the dataset and performed augmentation of data to enhance the accuracy.