## 2 Related Work
For extracting sentence features, the CNN model is paired with three attention methods. They concluded that the proposed CNN models were the most effective of all the models considered. According to [PERSON] and [PERSON] [26], the accuracy of the BiLSTM approach was 85.8% on the IMDB Movie Review and SST2 (Stanford Sentiment Treebank) datasets [27]. The authors demonstrated that the BiLSTM method is both more efficient and suitable for sentiment analysis problems. Word2 Vec, LSTM, RNN, and CNN methods were utilized by [PERSON] and colleagues [28] to extract emotions from Chinese hotel reviews. The model with the highest F-score, 92%, was the BiLSTM method. Some researchers have proposed hybrid deep learning-based models to improve accuracy, such as the LSTM-CNN grid-search (GS) approach for Amazon and IMDB reviews [29]. The authors utilized a grid-search technique and compared it to CNN, LSTM, CNN-LSTM, and other approaches. Their model outperformed several baseline models with an overall accuracy of 96%. In a similar study, the researchers [30] used Amazon reviews to model topics before using a CNN to identify views. The authors stated that their proposed approach improved the accuracy by 6 to 20% in comparison with the established methods. Further studies were conducted on the more efficient embedding approach, BERT, and its derivatives in enhancing the analysis of sentiments for user reviews. The authors of [31] employed BERTCNN to improve a sentiment analysis for commodities reviews, with the results stating that the BERT-CNN (F1-score of 84.3%) outperforms the BERT (82%) and CNN (84.3%) (70.9%) approaches. Similarly, in [32] the SenBERT-CNN (sentiment BERT-CNN) was proposed for analyzing the feedback for JD.com, a mobile phone supplier, by merging the BERT and CNN approaches to obtain deep characteristics of the dataset. When the LSTM, BERT, and CNN approaches were compared, the authors found that BERT-CNN worked the best, with a score or 95.7%. In [33], on the other hand, a dataset from Drugs.com was used to develop neural network models for predicting reviews of drugs. On a scale from 0 to 9, patients' levels of happiness were given scores between 0 and 9. The authors tested many neural network models, including the BERT-LSTM model, with the following methods: 10-class and 3-class compressed forms of the dataset. The results showed that the BERT-LSTM model was the best-suited for the 3-class setup, even though it took a very long time to train. Others examples include [34], who used BERT to train different NN models on a dataset of movie reviews. The results showed that BERTwas the most accurate, while [35] used BERT to analyze Twitter sentiments by turning jargon into plain text for BERT training. Additionally, in [36], the authors suggested a deep learning model using BERT for ADE (adverse drug effect) retrieval and detection to find pharmacological side effects. As a classifier and retrieval tool, the proposed model utilized sentence structure feature embeddings and BERT. Furthermore, in [37], the authors developed a method for extracting medical relations that relied on a pre-trained technique and a mechanism of fine-tuning rather than manual labeling. For feature extraction, the suggested method combined the BERT architecture with one-dimensional convolutional neural networks (1D-CNNs). The suggested method was tested on three datasets: the BioCreative V chemical relation corpus of illness, a classical Chinese literature dataset, and the i2b2 2012 temporal relation challenge dataset, and F1 score values of 0.7156, 0.8982, and 0.7085, respectively, were obtained. It was proposed by [PERSON] et al. [38] that an enhanced version of Sentic LSTM be used for a joint task that combined the target-dependent detection of aspects and targeted aspect-based polarity classification. In another study, Sentic LSTM was developed by [PERSON] et al. for the explicit integration of explicit and implicit information.