Abstract
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. 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. 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. Then, the sentences are sequentially integrated through the bidirectional gated recurrent unit (BiGRU) to extract the contextual semantic information features of the text. 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. The top-down text features of “word-sentence-text” are input into the softmax classifier to realize sentiment classification. Based on the weibo_senti_100 k dataset, the proposed model is experimentally demonstrated. 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. The proposed model can provide a certain reference for the related students’ text sentiment analysis research.
Highlights
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
People tend to express their own opinions on events at online social media. e student group especially occupies a large proportion of the Internet social platform users [1]
Compared with the traditional text sentiment analysis model, its innovations are summarized as follows: (1) Existing research often uses hand-designed feature extraction methods to extract text features, which cannot capture the complex language phenomena in the text. e proposed model uses a combination of CNN and bidirectional gated recurrent unit (BiGRU) to automatically learn the deep semantic information of the text from a large amount of data, which further ensures the accuracy of sentiment classification
Summary
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. E traditional machine learning models usually need to manually or automatically select features, and we can apply these machine learning models to classify the test data using these selected features [9, 10] Does this method require a large amount of data as a basis, and the characteristics of a certain dataset may not be adapted to other datasets. Most existing deep learning technologies cannot meet the requirements of high accuracy, and there is little research on student text sentiment analysis. To this end, a student text sentiment analysis model using the convolutional neural network-bidirectional gated recurrent unitattention mechanism (CNN-BiGRU-AT) model is proposed. E proposed model uses a combination of CNN and BiGRU to automatically learn the deep semantic information of the text from a large amount of data, which further ensures the accuracy of sentiment classification. (3) e proposed CNN-BiGRU-ATmodel has solved the problem of accuracy degradation caused by sample randomness
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