# Sentiment Analysis of Online New Energy Vehicle Reviews
## 4 Experiment and Analysis
### Baseline Model  
Different word embedding methods can have varying impacts on text classification tasks. Word embedding is a technique that maps words in text to real-valued vectors, providing continuous and dense representations of discrete words as input features for text classification models. To evaluate the effectiveness of the model, representative models based on word2 vec and BERT were chosen as baseline models for comparative experiments.  
(a) Word2 vec-based Word Embedding Methods  
FastText: FastText is a shallow neural network model used for text representation and classification. It aims to efficiently and rapidly process large-scale text data by leveraging word vectors.  
TextCNN: TextCNN is a text classification model based on Convolutional Neural Networks. It utilizes convolutional operations to extract features from text data, enabling efficient text classification.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter Name** & **Parameter Vaule** \\ \hline Maximum sentence length & 128 \\ Batch size & 64 \\ Transformer layers & 12 \\ Transformer number of hidden neurons & 768 \\ Learning rate & 0.00001 \\ Maximum number of iterations & 100 \\ Optimization methods & Adam \\ \hline \hline \end{tabular}
\end{table}
Table 2: Model-related parameter settings.  
TextRCNN: TextRCNN combines the strengths of recurrent neural networks (RNNs) and Convolutional Neural Networks (CNNs) to capture contextual information and local features in text sequences, leading to efficient text classification.  
TextRNN: TextRNN utilizes bidirectional LSTM networks to process information in both forward and backward directions, effectively incorporating contextual information from the text.  
TextRNN_Att: After extracting features using recurrent neural networks, TextRNN_Att employs an attention mechanism to assign varying levels of importance to the extracted features. It then uses feature vectors with different attention weights for classification.  
DPCNN: DPCNN is a text classification model based on Convolutional Neural Networks. It performs feature extraction on text through multiple layers of \"pyramid convolution\" operations, enabling efficient sentiment classification.  
Transformer: The Transformer model is a deep neural network model based on the self-attention mechanism. It determines whether a given text expresses positive sentiment, negative sentiment, or neutral sentiment.  
BERT-based Word Embedding Methods  
BERT_CNN: The BERT_CNN model employs BERT as a feature extractor to capture contextual semantic representations of the text. These representations are then input into a CNN for local feature extraction and representation.  
BERT_RNN: BERT is a pretrained language model based on self-supervised learning that effectively captures contextual information in text and generates contextual semantic representations. The BERT_RNN model combines the strengths of BERT's contextual semantic representations with the sequence modeling capabilities of RNNs, resulting in enhanced performances for text processing tasks.  
BERT_RCNN: BERT is a pretrained language model based on self-supervised learning, while RCNN is a text classification model based on Convolutional Neural Networks (CNNs) that extracts features from different positions in the text and employs fully connected layers for classification. By incorporating sliding windows and max pooling operations, RCNN captures local contextual information in the text.  
BERT_DPCNN: This model combines the advantages of both BERT and DPCNN. By utilizing the contextual semantic representations generated by BERT within DPCNN, it obtains multi-level feature representations. This approach helps capture diverse levels of semantic information in the text, thereby boosting the model's performance.