## 2 Sentiment Analysis Algorithms
### Machine Learning Approach
To achieve this goal, the multinomial naive Bayes method was used on a dataset consisting of 2000 Tweets labeled as positive or negative. The raw data were preprocessed through tokenization using 4-grams and stemming using the Khoja stemmer, before being represented as TF-IDF features and classified using a fivefold cross-validation process. The proposed method showed promising results with an accuracy of 87.5% on the dataset. [PERSON] (2020) [19] conducted a comprehensive study on the sentiment analysis of US airline Twitter data using six different machine learning models, including support vector machine (SVM), logistic regression, random forest, XgBoost, naive Bayes, and decision tree. The authors applied various preprocessing techniques, such as stop-word removal, punctuation removal, case folding, and stemming, to prepare the data for analysis. Feature extraction was carried out using the bag-of-words method on a dataset collected from CrowdFlower and Kaggle, consisting of 14,640 samples with three sentiment classes: positive, negative, and neutral. The results showed that SVM achieved the highest accuracy of 83.31%, followed by logistic regression with an accuracy of 81.81% when the dataset was split into a 70% training and 30% testing set. [PERSON] et al. (2021) [20] performed sentiment analysis related to violence against women using several machine learning models, including support vector machine (SVM), k-nearest neighbor, naive Bayes, and decision tree. The dataset was collected from Arabic Tweets and preprocessed using tokenization, stemming, and stop-word removal. Feature extraction was performed using the Term Frequency-Inverse Document Frequency (TF-IDF) method and the results showed that SVM achieved the highest accuracy of 78.25% on the self-collected dataset. In the study conducted by [PERSON] et al. (2021) [21], the authors aimed to develop a sentiment analyzer that could classify the polarity of text with high accuracy. To achieve this, they employed five different machine learning methods, including naive Bayes, multinomial naive Bayes, Bernoulli naive Bayes, logistic regression, and linear support vector classification. The experiments were conducted using the 'twitter samples' corpus in the Natural Language Toolkit, which consisted of 10,000 Tweets with equal numbers of positive and negative Tweets. Before feeding the data into the models, a thorough preprocessing phase was conducted, which included tokenization, stop-word removal, URL removal, symbol removal, case folding, and lemmatization. The results showed that the naive Bayes method achieved the highest accuracy of 99.73% on the corpus, making it the best-performing algorithm in the study. In conclusion, the authors of the reviewed studies mainly focused on data preprocessing, feature extraction, and a comparison of the accuracies of the different machine learning algorithms in sentiment analysis. The results showed that different algorithms achieved different levels of accuracy, ranging from 67% to 99.5%. Some of the highest-performing algorithms included maximum entropy, naive Bayes, and support vector machine. A summary of these conventional machine learning approaches can be found in Table 1.