A Sentiment Analysis Method for Teaching Evaluation Texts Using Attention Mechanism Combined with CNN-BLSTM Model

Abstract

In view of the problems that most existing emotion analysis models ignore the relationship between emotions and are not suitable for students, an emotion analysis model of teaching evaluation text based on deep learning is proposed. Firstly, combining the advantages of CNN extracting phrase features and BLSTM extracting sequence features, the CNN-BLSTM model is constructed to effectively enhance the extraction ability of text information. Then, the attention mechanism is used to adaptively perceive the context information, extract the text features that affect students’ emotion, and construct the CNN-BLSTM-AT model. Finally, the CNN-BLSTM-AT model is used to analyze the students’ emotion types in the dataset, and the mini-batch gradient descent method is used for model training. The experiment uses the weibo_senti_100k dataset to demonstrate the performance of the proposed model. The results show that adding the attention mechanism can improve the accuracy of the model by about 0.23. Also, its recall rate is not less than 0.57 and the minimum value of F1 is 0.748, which is better than other comparison models, thus demonstrating the effectiveness of the proposed model.

A Review of Sentiment Analysis Approaches for Quality Assurance in Teaching and Learning

“…From our ndings as illustrated in Table 5, we found out that 76% of the papers that were reviewed were based on extracting students' thoughts, opinion and attitudes toward teachers and 16% were based on extracting students' opinion towards courses and institution, whereas the remaining 8% were based on extraction student opinion towards institution. [9], [10], [11], [12], [13], [2], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29].…”

“…Seven (7) common database indexes which are Scopus, EBSCOhost, Science Direct, IEEE Xplore, Web Science, SpringerLink, and ACM DL were used to carry out the searches. The search strings are eleven (11) in total; they are "sentiment analysis", "opinion mining", "technologies used in sentiment analysis", "sentiment analysis framework", "sentiment analysis algorithms", "sentiment analysis tools", "students' feedback", "teacher assessment", "feedback assessment", "learners' feedback sentiment analysis reviews" and "quality assurance".…”

“…The majority of high impact journals and conferences are indexed in this collection of comprehensive databases. The eleven (11) search words were joined using Boolean 'OR'. As displayed in Table 1, a total of 3,392 articles from the seven databases were retrieved.…”

“…NB [9], [12], [15], [16], [17], [20], [21], [22], [23], [24], [25] NN [9], [11], [13], [14], [17], [19], [23], [38], [41], [59], [61].…”

Using sentiment analysis to study the relationship between subjective expression in financial reports and company performance

“…This group of experiments uses CASIA data to compare the four models. To prove the ability of the attention mechanism to identify time series features, the CNN-LSTM model proposed in the literature Peng et al, 2022 is used for the first set of comparisons. The second set of comparisons uses the AC-BiLSTM model Dong et al, 2020, and comparisons uses the Self-Attention-BiGRU model Qiu et al, 2020 to ensure accuracy and stability.…”