Sentiment Analysis of MOOC Reviews Based on Knowledge Dependency Tree

As an important online learning resource, Massive Open Online Courses have a large amount of comments, which can be exploited by aspect-level sentiment analysis to optimize MOOC teaching from different perspectives. However, there are two essential problems. One is that there is no open-source dataset on Chinese MOOC. The other problem is semantic information confusion caused by inherent polysemy of Chinese words and ambiguous expressions relatively relying on the context. In order to further characterize the special features of Chinese MOOC reviews, we build an open-source dataset with clean 5000 MOOC reviews and propose a sentiment knowledge dependency tree based graph neural network. The proposed model firstly uses the latest term frequency–inverse document frequency algorithm to extract high-frequency words, and combines it with the Semantic Orientation Pointwise Mutual Information algorithm so that a sentiment dictionary in the field of Chinese MOOCs is constructed. Then, the grammatical information of the dependency tree is merged with the sentiment knowledge information of the sentiment dictionary. Next, this novel model uses GCN to capture the long-distance feature information of the sentiment dependency tree, and finally adopts the softmax function for sentiment classification. To further improve the model's performance, we also use Bert to enhance the text representation for higher accuracy. Meanwhile, the comparative experiments demonstrate that our proposed model takes advantages of the customized dependency tree by knowledge dictionary to achieve more accurate sentiment analysis than the state-of-the-art methods under different word embedding approaches.