## 2 Sentiment Analysis Algorithms
### Deep Learning Approach
The results indicated that the hybrid CNN and LSTM model with GloVe embedding achieved an accuracy of 59%. Similarly, [PERSON] and [PERSON] (2020) [31] proposed a hybrid model that combined CNN and LSTM for sentiment analysis. The dataset used in the study comprised 100 restaurant reviews collected from the Foodpanda and Shohoz Food apps. The preprocessing steps involved removing unimportant words and symbols, as well as converting the text into embeddings using the word2 vec algorithm. The proposed hybrid model consisted of multiple layers, including an embedding layer with the word2 vec pretrained model, a convolutional layer, max-pooling layer, LSTM layer, dropout layer, and classification layer. The model achieved an impressive accuracy of 75.01% on the self-collected dataset. [PERSON] et al. (2020) [32] presented a hybrid model that combined a CNN with bidirectional long short-term memory (BiLSTM) for sentiment analysis on the Sentiment140 dataset. The dataset comprised 1.6 million positive and negative Tweets and underwent preprocessing steps such as case folding and stemming and the removal of stop words, numbers, URLs, Twitter usernames, and punctuation marks. The proposed hybrid model consisted of multiple layers, including an embedding layer with the GloVe pretrained model, a one-dimensional convolutional layer, a BiLSTM layer, fully connected layers, dropout layers, and a classification layer. The results indicated that the model achieved an accuracy of 81.20% on the Sentiment140 dataset. The study by [PERSON] et al. (2019) [33] presented a hybrid model that combined the strengths of convolutional neural networks (CNNs) and bidirectional long short-term memory (BiLSTM) networks for sentiment analysis. The experiments were conducted on a dataset that contained 2003 articles and news articles with positive, negative, and neutral sentiments. The text data were preprocessed by converting them into word embeddings using a pretrained doc2 vec model. The proposed hybrid model comprised several layers, including a convolutional layer, max-pooling layer, BiLSTM layer, dropout layer, and classification layer. The experiments showed that the hybrid model achieved an impressive accuracy of 90.66% on the dataset. [PERSON] et al. (2020) [34] improved on the hybrid CNN and BiLSTM model by incorporating an attention mechanism. The experiments were conducted on the IMDb dataset, which contained 50,000 positive and negative reviews. The text data were preprocessed by encoding them using a word2 vec pretrained embedding model. The proposed hybrid model was further optimized using techniques such as the Adam optimizer, L2 regularization, and dropout. The results showed that the proposed model achieved an accuracy of 90.26% on the IMDb dataset, demonstrating the effectiveness of the attention mechanism in sentiment analysis. [PERSON] et al. (2020) [35] developed a hybrid model that combined CNNs and BiLSTMs for sentiment analysis. The model was constructed using several key components, including a convolutional layer that consisted of 3 filters with a filter size of 128 and a ReLU activation function, a max-pooling layer with a pool size of 3, and a dropout layer with a rate of 0.2 to prevent overfitting. The experiments were conducted on a dataset that contained 10,401 comments in English, Kannada, and a mixture of both languages. The text data underwent preprocessing steps such as case folding and the removal of digits, special characters, symbols, and duplicated comments. The proposed convolutional BiLSTM model achieved an accuracy of 77.6% on the dataset, demonstrating its effectiveness in sentiment analysis on multilingual data. [PERSON] et al. (2019) [36] presented a hybrid model that combined a one-dimensional convolutional neural network (1D-CNN) and an RNN for sentiment analysis. The model was evaluated on the IMDb dataset, which consisted of 50,000 movie reviews with positive and negative sentiments. The 1D-CNN part of the model included 128 and 256 filters for its convolutional layers.