## 3 Related Work
### Sentiment Analysis Methods Based on Semantic Analysis  
For sentiment analysis in social media, combining additional content with semantic information has been a hot issue. For example, [PERSON] et al. suggested a neural network including conventional economic methodologies to examine public opinion on social media [17]. Their findings suggest that large-scale public opinion on the internet can impact or forecast changes in commodity prices. Sentiment dictionaries are used in the current mainstream technique to add semantic information. Based on a recurrent neural network (RNN), [PERSON] et al. proposed a new model by combining lexicons with a bi-directional long short-term memory neural network (BiLSTM) for text classification [18]. To estimate the sentiment polarity, dictionary-based methods generally evaluate the semantic pointing of each sentiment word in the text span. However, sentiment dictionaries often need manual involvement, resulting in sentiment dictionaries that do not keep up with the ever-changing social media. To overcome these problems, the researchers used knowledge bases to assist in sentiment analysis. [PERSON] et al. proposed a knowledge-based methodology for sentiment analysis on social media [19]. Their research analyses the text and applies semantic processing to the comments' implicit knowledge. [PERSON] employs knowledge bases to expand the vocabulary set, enhancing the system's performance by reducing the number of unseen terms [20]. His method efficiently translates the semantic representations of unseen words in social media to a domain-specific trained embedding space. The approaches mentioned above only use root domain single-source knowledge graphs to perform sentiment classification in social media, which frequently needs knowledge graphs from other data sources. Clustering the entities in different knowledge graphs is an urgent problem for them to solve. To get a more precise assessment of emotional polarity, researchers have employed fine-grained approaches for sentiment analysis. Sentic-LSTM was suggested by [PERSON] et al. as an extension of the LSTM model for sentiment analysis based on aspects [21]. Their research focuses on integrating tasks, such as object-related aspect detection and aspect-based polarity classification. They also used commonsense knowledge to integrate it into a recurrent encoder for target sentiment analysis [22]. Their model is a hybrid of attention architecture and Sentic-LSTM. Attention mechanisms have been successfully used for a variety of NLP tasks. Conventional attention mechanisms cannot distinguish target-sensitive sentiment expression. [PERSON] et al. presented the Multi-Head Self-Attention Transformation (MSAT) network, which uses target-specific self-attention and dynamic target representation to perform more effective sentiment analysis [23]. Even though researchers have made great progress in fine-grained sentiment analysis, the current problem of a lack of huge data that is appropriate for fine-grained sentiment analysis remains, making it challenging to translate experimental results into widespread practical applications. Considering that aspect-based sentiment analysis research needs specialized datasets, current sentiment analysis approaches seek to pre-train models using a combination of large data. [PERSON] et al. proposed a deep learning approach based on pre-trained language representation models. They combined the results of robustly optimized BERT with the generalized autoregressive language model XLNET for language understanding to obtain better results for sentiment analysis [24; 25; 26]. [PERSON] et al. took the embeddings of the pre-training model fastText, which trains on big data of tweets, to analyze the sentiment of people in different countries under the new crown epidemic [27; 28]. [PERSON] et al. suggest a novel effective technique of sentiment analysis based on deep learning architectures that combine fine-tuning the universal language model and SVM to improve sentiment analysis [29].