With the continuous expansion of social network user groups, tens of thousands of data are generated per second on social platforms. If you can collect these comment texts and analyze the emotional tendencies of the student groups, you can understand their emotional state for certain events and provide strong support for subsequent decision-making. People’s evaluation often contains emotional tendencies. The student group especially occupies a large proportion of the Internet social platform users. People tend to express their own opinions on events at online social media. In recent years, Internet social networking, especially mobile Internet social networking platforms, has rapidly emerged around the world, and online social platforms such as Facebook and Weibo have emerged. The proposed model can provide a certain reference for the related students’ text sentiment analysis research. The results show that the accuracy rate and recall rate of its classification mostly exceed 0.9, and the F1 value is not lower than 0.8, which are better than the results of other models. Based on the weibo_senti_100 k dataset, the proposed model is experimentally demonstrated.
The top-down text features of “word-sentence-text” are input into the softmax classifier to realize sentiment classification.
Finally, an attention mechanism is added to the CNN-BiGRU model, and different learning weights are applied to the model by calculating the attention score. Then, the sentences are sequentially integrated through the bidirectional gated recurrent unit (BiGRU) to extract the contextual semantic information features of the text. Firstly, the text is divided into multiple sentences, and the convolutional neural network (CNN) is used to extract n-gram information of different granularities from each sentence to construct a sentence-level feature representation. A novel student text sentiment analysis model using the convolutional neural network with the bidirectional gated recurrent unit and an attention mechanism, called CNN-BiGRU-AT model, is proposed. For most current sentiment analysis models, it is difficult to capture the complex semantic and grammatical information in the text, and they are not fully applicable to the analysis of student sentiments.