# BERT- and BiLSTM-Based Sentiment Analysis of Online Chinese Buzzwords
## 2 Related Work  
Currently, researchers are focused on studying text sentiment analysis based on sentiment lexicons [7], traditional machine learning [8], and deep learning [9; 10]. The sentiment-lexicon-based approach constructs the sentiment lexicon manually and sets the matching rules manually to finally achieve the sentiment analysis of the text. For example, the SentiWordNet [11] sentiment lexicon first combines synonyms based on WordNet and then assigns a positive or negative polarity score to a set of synonyms, which can represent the user's sentiment tendency. Unlike English sentiment lexica, Chinese sentiment lexica are mainly composed of NTUSD [12], the Zhiwang knowledge base, and sentiment vocabulary ontology databases. [PERSON] et al. [13] improved the semantic similarity algorithm based on the knowledge network words, thus improving the semantic similarity accuracy. [PERSON] et al. [14] merged a lexicon of microblog data based on Hownet word similarity and subsequently used pointwise mutual information (PMI) to classify the sentiment of web words. [PERSON] et al. [15] combined the CBOW model and syntactic rules to extract candidate sentiment words from the corpus. They then used the improved SOPMI algorithm to determine the sentiment polarity to form a domain positive- and negative-sentiment lexicon. Collecting and summarizing words with sentiment tendency requires a great deal of time and reading many related materials as well as marking the sentiment polarity and intensity of these words in different degrees. Therefore, the cost of building sentiment dictionaries is large, and sentiment-dictionary-based methods do not consider the relationships between words in the text and lack word sense information. Machine learning methods show advantages in sentiment analysis tasks compared to sentiment polarity dictionaries. [PERSON] [16] used the TF-IDF algorithm to convert text data into vector data for describing the sentiment of different movie reviews. Three machine learning models were trained by these feature vectors, L1 logistic regression, L2 logistic regression, and a CatBoost model. The results showed that the accuracy and precision of the L1 logistic regression was close to 80%. [PERSON] [17] used plain Bayesian (NB) and maximum entropy (ME) methods with a support vector machine (SVM) to perform a review sentiment analysis of tendencies. Since feature selection affects the performance of machine learning methods, [PERSON] [18] analyzed online review comments by combining N-gram models with machine learning methods. Their experiments showed that SVM combined with unigram, bigram, and trigram to extract features achieved the best classification results. With the research and development of deep learning in natural language processing, deep neural networks have achieved outstanding performance in sentiment analysis. [PERSON] [19] solved the sentiment classification problem using convolutional neural networks (CNNs). [PERSON] [20] proposed gated recurrent units (GRUs) to analyze contexts with long dependency problems, which showed significant improvements in various tasks of natural language processing. [PERSON] and [PERSON] [21] proposed a model for sentiment analysis based on hierarchical attention networks with a 5% improvement in accuracy compared to recurrent neural networks. To address the inability of traditional neural networks to fully capture the entire context of a sentence or comment, some studies have used variants of recurrent neural networks (RNN) [22], such as LSTM or GRU, to solve sentiment analysis problems [23]. LSTM models enable phrase-level sentiment classification to include linguistic regularizations, such as negativity, intensity, and polarity [24]. [PERSON] [25] used bidirectional LSTM to train the labeled text in order to process the syntax and semantics of Japanese. For polarity classification of phrase-level words, [PERSON] [26] proposed a BiGRU model with multiple inputs and multiple outputs.