# CWSXLNet: A Sentiment Analysis Model Based on Chinese Word Segmentation Information Enhancement
###### Abstract
The BiLSTM model can be thought of as processing the input sequence from left to right for one LSTM model and from right to left for another LSTM model, and finally merging their outputs. The advantage of this is that not only the previous information but also the subsequent information can be considered when processing the input at the current time step. [PERSON] et al. in [13] used a bidirectional LSTM (BiLSTM) model for sentiment analysis. The experimental results show that BiLSTM outperforms CRF and LSTM for Chinese sentiment analysis. Similarly, [PERSON] et al. in [14] proposed a scalable multi-channel extended CNN-BiLSTM model with attention mechanism for Chinese text sentiment analysis, in which the convolutional model CNN based on bridging the BiLSTM model and achieved better result on several public Chinese sentiment analysis datasets. In addition to LSTM, some researchers select GRU (Gated Recurrent Unit) to handle sentiment analysis tasks. As a variant of LSTM, GRU has fewer parameters than LSTM, requires less training data and has a faster training speed. [PERSON] et al. in [15] proposed the adoption of the application of CNN-BiGRU model in Chinese short text sentiment analysis, which introduced the BiGRU model based on CNN. [PERSON] et al. in [16] proposed Transformer-Encoder-GRU (T-E-GRU) to solve the problem of transformer being naturally insufficient compared to the recurrent model in capturing the sequence features in the text through positional encoding. Both have achieved good experimental results in the field of Chinese sentiment analysis. To leverage the affective dependencies of the sentence, in 2020, [PERSON] et al. in [17] proposed a graph convolutional network based on SentiNet [18] according to the specific aspect, called Sentic GCN, and explored a novel solution to construct the graph neural net works via integrating the affective knowledge from SenticNet to enhance the dependency graphs of sentences. Experimental results illustrate that SenticNet can beat state-of-the-art methods. In the same year, [PERSON] et al. in [19] proposed BBSO-FCM model for sentiment analysis, used Binary Brain Storm Optimization (BBSO) algorithm for the Feature Selection process and thereby achieved improved classification performance, and Fuzzy Cognitive Maps (FCMs) were used as a classifier to classify the incidence of positive or negative sentiments. Experimental values highlight the improved performance of BBSO-FCM model in terms of different measures. In 2021, [PERSON] et al. in [20] proposed three different feature extraction methods and three different CNNs (Convolutional Neural Networks) to implement the features using a low resource dataset called NepCOV19 Tweets, which contains COVID-19-related tweets in Nepali language. By using ensemble CNN, they ensemble the three CNNs models. Experimental results show that proposed feature extraction methods possess the discriminating characteristics for the sentiment classification, and the proposed CNN models impart robust and stable performance on the proposed features. However, machine learning-based methods require large amounts of annotated data to train the classifier, and require manual selection of features and algorithms. There is a degree of subjectivity in feature extraction and algorithm selection, with good or bad feature extraction directly affecting classification results [21] and not easily generalized to a new corpus. Deep learning-based approaches: In recent years, the rise of deep learning techniques has brought new breakthroughs in text sentiment analysis. In particular, the use of pre-trained language models (e.g., BERT, RoBERTa, XLNet, etc.) for fine-tuning to solve sentiment analysis problems has yielded very good results. This approach does not require manual feature construction and can handle complex semantic relationships, and therefore has very promising applications in the field of text sentiment analysis.