# A New Sentiment-Enhanced Word Embedding Method for Sentiment Analysis
## 4 Experiment
### Comparison Experimental Models and Their Training Details  
TextCNN [22], TextRNN [23], TextRCNN (our main reference codes come from: [[https://github.com/649453932/Chinese-Text-Classification-Pytorch](https://github.com/649453932/Chinese-Text-Classification-Pytorch)]([https://github.com/649453932/Chinese-Text-Classification-Pytorch](https://github.com/649453932/Chinese-Text-Classification-Pytorch)), accessed on 5 December 2021) [24], ABCDM [25], and CNN-LSTM [26] were selected as sentiment classification models to verify the effectiveness of trained sentiment-enhanced word embeddings by the S-EWE method in fused and final lexicons.  
**TextCNN.** TextCNN [22] convolves word vectors in the convolution kernel. It obtains the feature maps of these convolution results through max pooling.  
**TextRNN.** TextRNN [23] is a bidirectional LSTM. It inputs word embeddings and outputs the hidden state of each word.  
**TextRCNN.** TextRCNN [24] uses LSTM (resp. BiLSTM) to obtain the forward (resp. bidirectional) hidden state. It applies max pooling to get sentence embeddings.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{2}{c}{**SemEval-2013 (Binary) [27]**} & \multicolumn{3}{c}{**SST-2 [28]**} \\ \cline{2-7}  & **Positive** & **Negative** & **Total** & **Positive** & **Negative** & **Total** \\ \hline Train & 2974 & 1159 & 4133 & 3610 & 3310 & 6920 \\ Develop & 483 & 280 & 763 & 444 & 428 & 872 \\ Test & 1281 & 472 & 1753 & 912 & 909 & 1821 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The details of SemEval-2013 and SST-2 datasets.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Datasets** & **Total Words** & **Positive Words** & **Negative Words** & **Neutral Words** \\ \hline SemEval-2013 & 15,133 & 2755 & 1412 & 10,966 \\ SST-2 & 16,008 & 4001 & 2994 & 9013 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The numbers of sentiment words in SemEval-2013 and SST-2.  
**ABCDM.** ABCDM [25] adopts BiLSTM and BiGRU to encode one sentence to obtain long-term and short-term hidden states. Then, it uses two parallel attention layers to obtain the attention score of each hidden state. Finally, ABCDM applies 1D-CNN, 1D-max pooling, and 1D-average pooling to get the final sentence embedding.  
**CNN-LSTM.** CNN-LSTM [26] uses TextCNN [22] to obtain local sentence embedding, and adopts LSTM to get the long-term information.  
In these sentiment classification models, Adam [52] optimizer is regarded as the optimizer. The cross-entropy function is selected as the loss function. The classical feature-based word embedding methods, including Word2 Vec [1] and GloVe [2], are used to pre-train word embeddings. Specifically, GoogleNews-vectors-negative300 ([[https://drive.google.com/file/d/0B7](https://drive.google.com/file/d/0B7) XkCwpD5 KDYNINUUTTISS21 pQmM/edit?resourcekey=0-wjGZdNAUop6 WykTtMip30g]([https://drive.google.com/file/d/0B7](https://drive.google.com/file/d/0B7) XkCwpD5 KDYNINUUTTISS21 pQmM/edit?resourcekey=0-wjGZdNAUop6 WykTtMip30g), accessed on 5 November 2021 and glove.840B.300d ([[https://nlp.stanford.edu/data/glove.840B.300d_zip](https://nlp.stanford.edu/data/glove.840B.300d_zip)]([https://nlp.stanford.edu/data/glove.840B.300d_zip](https://nlp.stanford.edu/data/glove.840B.300d_zip)), accessed on 10 December 2021) were adopted. The embedding dimensions of Word2 Vec and GloVe were 300. All parameters of the S-EWE method and downstream task models are shown in Table 4.