## 5 Conclusions and Future Work  
Online education and online course learning have become important learning methods for all classes after the rapid development of the epidemic. However, there are still many problems in online courses, and many teaching methods or the quality of courses cannot meet the requirements of learners. As key data reflecting the quality of online courses, sentiment recognition of user reviews is of great significance for evaluation and online courses. Emotional analysis of curriculum evaluation texts is helpful to identify effective curriculum suggestions, promote curriculum perfection and improvement, and is of great significance to improve the quality of online education. Considering the complexity of network evaluation, the single-channel word vector may lead to information loss, and different deep network feature mining focuses are different, this paper proposes a two-channel deep network sentiment analysis model based on the MOGWO ensemble method, which abandons the sequential extraction of deep features by CNN and bidirectional LSTM. The information loss of sequence features in the convolution layer is avoided. The experimental results showed that this model has high accuracy and stability in the identification of emotional polarity and degree. On the other hand, this paper used Wikipedia's large corpus to pre-train Word2 Vec and GloVe word embedding models to ensure that word vectors can obtain sufficient training information, which is of great significance for subsequent sentiment analysis. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{\begin{tabular}{c} **Evaluation** \\ **Metrics** \\ \end{tabular} } & \multicolumn{6}{c}{**Mood States**} \\ \cline{3-8}  & & \begin{tabular}{c} **Great** \\ **Satisfaction** \\ \end{tabular} & **Satisfaction** & **Neutral** & **Disappointment** & \begin{tabular}{c} **Great** \\ **Disappointment** \\ \end{tabular} & **Average** \\ \hline \multirow{3}{*}{\begin{tabular}{c} The proposed \\ model \\ \end{tabular} } & Accuracy & 0.9760 & 0.9600 & 0.9700 & 0.9580 & 0.9720 & 0.9672 \\  & Precision & 0.9783 & 0.8846 & 0.9126 & 0.8692 & 0.9574 & 0.9204 \\  & Recall & 0.9000 & 0.9200 & 0.9400 & 0.9300 & 0.9000 & 0.9180 \\  & F1 & 0.9375 & 0.9020 & 0.9261 & 0.8986 & 0.9278 & 0.9184 \\ \hline \multirow{3}{*}{\begin{tabular}{c} Seq2 seq- \\ attention \\ \end{tabular} } & Accuracy & 0.9440 & 0.9320 & 0.9280 & 0.9280 & 0.9520 & 0.9368 \\  & Precision & 0.8750 & 0.8367 & 0.7963 & 0.8019 & 0.9130 & 0.8446 \\  & Recall & 0.8400 & 0.8200 & 0.8600 & 0.8500 & 0.8400 & 0.8420 \\  & F1 & 0.8571 & 0.8283 & 0.8269 & 0.8252 & 0.8750 & 0.8425 \\ \hline \multirow{3}{*}{\begin{tabular}{c} TextCNN \\ \end{tabular} } & Accuracy & 0.9480 & 0.9320 & 0.9200 & 0.9260 & 0.9460 & 0.9344 \\  & Precision & 0.9022 & 0.8113 & 0.7727 & 0.8058 & 0.9101 & 0.8404 \\  & Recall & 0.8300 & 0.8600 & 0.8500 & 0.8300 & 0.8100 & 0.8360 \\  & F1 & 0.8646 & 0.8350 & 0.8095 & 0.8177 & 0.8571 & 0.8368 \\ \hline \multirow{3}{*}{\begin{tabular}{c} Bilstm \\ \end{tabular} } & Accuracy & 0.9480 & 0.9220 & 0.9360 & 0.9180 & 0.9360 & 0.9320 \\  & Precision & 0.9111 & 0.7850 & 0.8333 & 0.7757 & 0.8617 & 0.8334 \\  & Recall & 0.8200 & 0.8400 & 0.8500 & 0.8300 & 0.8100 & 0.8300 \\  & F1 & 0.8632 & 0.8116 & 0.8416 & 0.8019 & 0.8351 & 0.8307 \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} SVM \\ \end{tabular} } & Accuracy & 0.9380 & 0.9080 & 0.9020 & 0.9180 & 0.9340 & 0.9200 \\  & Precision & 0.8876 & 0.7368 & 0.7297 & 0.8041 & 0.8764 & 0.8069 \\  & Recall & 0.7900 & 0.8400 & 0.8100 & 0.7800 & 0.7800 & 0.8000 \\  & F1 & 0.8360 & 0.7850 & 0.7678 & 0.7919 & 0.8254 & 0.8012 \\ \hline \multirow{3}{*}{
\begin{tabular}{c} RNN \\ \end{tabular} } & Accuracy & 0.9300 & 0.9220 & 0.9140 & 0.9200 & 0.9420 & 0.9256 \\  & Precision & 0.8495 & 0.8020 & 0.7664 & 0.7885 & 0.8737 & 0.8160 \\ \cline{1-1}  & Recall & 0.7900 & 0.8100 & 0.8200 & 0.8200 & 0.8300 & 0.8140 \\ \cline{1-1}  & F1 & 0.8187 & 0.8060 & 0.7923 & 0.8039 & 0.8513 & 0.8144 \\ \hline \hline \end{tabular}
\end{table}