## 2 Sentiment Analysis Mechanisms
### Lexicon-Based Approach
As proposed in [21], a lexicon-based mechanism was presented for analyzing Arabic polarity terms from a Twitter dataset. The proposed method applied a mechanism for distributing different multi-weight polarities based on the number of detected polarity terms in the same sentiment. Therefore, if the number of detected terms increases, the weight polarity will increase due to the diversityand orientation of polarity terms. As presented in [22; 23], a set of positive and negative sentences were assigned based on Arabic tweets, where a hybrid strategy was proposed to combine different machine learning approaches. The Lexical-based classifier applied this method to label the training data. As presented in [24], an automatic sentiment analysis based on supervised classification on the Arabic dataset was proposed. Most sentiment analysis methods verify the sentiments based on their polarity. When the negation term in the sentiment is detected immediately before the polarity term, the sentiment polarity is converted to its reverse polarity. The authors in this paper proposed a methodology for detecting negation terms even if they do not precede the polarity term. Another enhanced sentiment analysis mechanism for analyzing Arabic reviews was proposed in [25], where a lexicon-based analyzer was constructed to analyze polarity terms with different weights to increase efficiency. Aspect-based sentiment analysis was proposed in [26], where a model was provided for estimating the polarity of user reviews. A lexicon was constructed for acquiring tweets from Twitter, and the dataset was preprocessed to remove any stop words and non-English words. The subjective sentences were processed using Senti-Word-Net to apply a score for each sentence. Based on the provided score, the aspect of each sentence was verified. Another recent research for applying aspect and content analysis of sentiments is presented in [27]. The authors provided a framework for collecting marketing and customer service information from reviews of different universities. The published or posted content of each university was classified into links, photos, videos, and statuses and then the frequency of each content was defined and scored. The authors of [28] provided a framework for analyzing sentiment opinions during the COVID-19 outbreak. The dataset was collected, classified based on seven clusters, and categorized based on five annotators. The positive, negative, and neutral polarities were determined in each cluster, and the polarity score for each cluster was defined. Another methodology for analyzing sentiments during COVID-19 was proposed in [29]. The authors aimed to examine and measure the influencing factors that affect the orientation of people during the epidemic. The dataset was collected from different social media forums. Based on the proposed influencing factors, the dataset was classified, and the sentiments were analyzed to explore their polarities. The authors of [30] proposed another method of NLP for analyzing user sentiments during the COVID-19 epidemic. A framework was proposed to measure the performance of a set of relevant word terms from different aspects such as economy, social, and health to view the major orientation of the reviews that explained a neutral polarity orientation.