## 2 Sentiment Analysis Algorithms
### Deep Learning Approach
The RNN part consisted of LSTM, Bi-LSTM, and GRU, with 128 units in each layer. The results showed that the 1D-CNN with the GRU model outperformed the other models, achieving an accuracy of 90.02% on the IMDb dataset. [PERSON] et al. (2020) [37] proposed a hybrid model consisting of a CNN and an RNN for sentiment analysis. The model was trained and evaluated on a dataset of 12,500 positive and negative movie reviews. The text was preprocessed by removing stop words and punctuation and then represented using the GloVe pretrained model. The proposed convolutional RNN model achieved an accuracy of 84% on the movie review dataset. [PERSON] et al. (2020) [38] introduced a Bi-LSTM model for sentiment analysis on Twitter data. The model was trained and evaluated on the Twitter US Airline Sentiment dataset, which is publicly available on Kaggle. The text data were preprocessed by applying techniques such as tokenization and stop-word and punctuation removal. The text was then represented using three different word embeddings--word2 vec, GloVe, and sentiment-specific word embedding. The results showed that the Bi-LSTM model achieved an accuracy of 81.20% on the Twitter US Airline Sentiment dataset, demonstrating its effectiveness for sentiment analysis on Twitter data. [PERSON] and [PERSON] (2021) [39] proposed a Bi-LSTM model for analyzing the sentiment of Amazon e-commerce reviews. The authors collected a dataset of 23,485 reviews, which were divided into three classes based on the rating points: negative, neutral, and positive. Data cleaning was carried out by tokenizing the data and removing special characters, followed by the generation of word embeddings using the word2 vec pretrained model. The BiLSTM model achieved an impressive accuracy of 90.26% on the self-collected dataset. Similarly, [PERSON] and [PERSON] (2020) [40] utilized a character-based deep bidirectional long short-term memory (DBLSTM) method for analyzing the sentiment of Tamil Tweets. The authors collected a dataset of 1500 Tweets, which were divided into positive, negative, and neutral classes. The data were preprocessed to remove unnecessary symbols and characters and the cleaned data were represented using the word2 vec pre-trained model as the word embedding for the DBLSTM method. The model achieved an accuracy of 86.2% on the Tamil Tweets dataset. Furthermore, [PERSON] and [PERSON] (2020) [41] proposed a sentiment analysis bidirectional long short-term memory (SAB-LSTM) model. The model consisted of 196 Bi-LSTM units, 128 embedding layers, 4 dense layers, and a classification layer with a softmax activation function. The authors used a dataset of 80,689 samples from 5 sentiment classes collected from various sources such as Twitter, YouTube, Facebook, news articles, etc. The dataset was divided into a 90% training set and a 10% testing set and the experiments revealed that the SAB-LSTM model outperformed traditional LSTM models in sentiment analysis. [PERSON] et al. (2021) [42] explored the use of RNNs for sentiment analysis of customer reviews collected from hotel booking websites. In their study, the authors employed various preprocessing techniques, such as lemmatization and stemming, as well as punctuation and stop-word removal, to clean the data before feeding it into two deep learning models: long short-term memory (LSTM) and a gated recurrent unit (GRU). The LSTM model had 30 hidden layers, whereas the GRU model had 25 hidden layers. The results of the experiments showed that the LSTM and GRU models achieved accuracies of 86% and 84%, respectively, on the self-collected dataset. In another study, [PERSON] et al. (2020) [43] investigated the performance of two deep learning models, multilingual BERT (mBERT) and XLM-RoBERTa (XLM-R), for sentiment analysis of multilingual social media text. The dataset used in this study was scraped from Twitter during the 2018 general election in Pakistan and consisted of 20,375 Tweets in both English and Roman Urdu.