Research on Sentiment Analysis Algorithm for Comments on Online Ideological and Political Courses

The online course teaching platform provides a more accessible and open teaching environment for teachers and students. The sentiment tendency reflected in the online course comments becomes an essential basis for teachers to adjust the course and students to choose the course. This paper combined two deep learning algorithms, i.e., a convolutional neural network (CNN) algorithm and a long short-term memory (LSTM) algorithm, to identify and analyze the emotional tendency of comments on online ideological and political courses. Moreover, the CNN+LSTM-based sentiment analysis algorithm was simulated in MATLAB software. The influence of the text vectorization method on the recognition performance of the CNN+LSTM algorithm was tested; then, it was compared with support vector machine (SVM) and LSTM algorithms, and the comments on online ideological and political courses were analyzed. The results showed that the recognition performance of the CNN+LSTM-based sentiment analysis algorithm adopting the Word2vec text vectorization method was better than that adopting the one-hot text vectorization method; the recognition performance of the CNN+LSTM algorithm was the best, the LSTM algorithm was the second, and the SVM algorithm was the worst in terms of the performance of recognizing the sentiment of comment texts; 86.36% of the selected comments on ideological and political courses contained positive sentiment, and 13.64% contained negative sentiment. Relevant suggestions were given based on the negative comments.

@article{Zhang2022,
  title     = {Research on Sentiment Analysis Algorithm for Comments on Online Ideological and Political Courses},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {11},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Xiang Zhang and Xiaobo Qin},
  doi       = {10.14569/IJACSA.2022.0131119},
  url       = {https://doi.org/10.14569/IJACSA.2022.0131119}
}