Student sentiment classification model based on GRU neural network and TF-IDF algorithm

Abstract

Due to the diversity of text expressions, the text sentiment classification algorithm based on semantic understanding is difficult to establish a perfect sentiment dictionary and sentence matching template, which leads to strong limitations of the algorithm. In particular, it has certain difficulties in the classification of student sentiments. Based on this, this paper analyzes the student sentiment classification model by neural network algorithm and uses the student group as an example to explore the application of neural network model in sentiment classification. Moreover, the regularization method is added to the loss function of LSTM so that the output at any time is related to the output at the previous time. In addition, the sentimental drift distribution of sentimental words on each sentimental label is added to the regularizer, and the sentimental information is merged with the two-way LSTM to allow the model to choose forward or reverse. Finally, in order to verify the research model, the performance of the model proposed in this paper is studied through experimental research. The research shows that the model proposed in this paper has better comprehensive performance than the traditional model and can meet the actual needs of students’ sentiment classification.

Deep Learning-Based Text Sentiment Analysis in Chinese International Promotion

“…Wang et al used a long short-term memory network for Twitter sentiment classification by simulating word interactions in the synthesis process [21]. Huang et al proposed encoding grammatical knowledge in a tree-shaped long-and short-term memory network to enhance the representation of phrases and sentences [22]. Teng et al proposed a contextsensitive lexicon-based sentiment analysis method using a bidirectional long-and short-term memory network to learn sentiment intensity [23].…”

An Emotional Analysis Method Based on Multi Model Ensemble Learning

“…TF-IDF is a statistical method used to evaluate the importance of a word to a document in a corpus. TF (term frequency) represents word frequency [23]. The definition of probability representation for TF is as follows.…”

A Multi-Dimensional Text Sentiment Analysis Method Based on Joint Network

“…When the convolution kernel window size is set to 5, the prediction accuracy is the highest. Figure 2(b) shows the prediction accuracy when using different combinations of convolution kernels of different sizes, and it can be seen that when the combination of convolution kernel window sizes is (3,4,5), the capsule network obtains higher prediction accuracy.…”

“…Yang et al [3] combined the BERT model with the capsule network and proposed an enhanced capsule network to accurately feedback the real word-of-mouth of the movie through the user's comments in the sentiment analysis based on social media comments. The BiGRU model is composed of two GRU models in opposite directions superimposed up and down [4], and the single GRU model can only obtain the one-way above or below information of the text [5]. In this paper, BiGRU is used instead of GRU to better capture the bidirectional semantic dependence of the text.…”

“…b is the bias vector. For a combination of B convolution kernels containing C kernels of different sizes, the obtained output is shown in Equation(5).…”