การพัฒนาโมเดลการวิเคราะห์อารมณ์ครูในชั้นเรียนผ่านการรู้จำคำพูดโดยใช้การเรียนรู้เชิงลึก - จิระเมศร์ รุจิกรหิรัณย์ (จุฬาลงกรณ์มหาวิทยาลัย, 2022)
This study has 2 objectives. (1) To create a model for analysing teacher emotions in the classroom using machine learning and deep learning approaches to analyse the sentiment of text derived from speech recognition. (2) To assess the model s applicability for analysing teacher emotions in the classroom. A total of 23,974 text samples from two sources were used in the study. These contained 11 films of teachers teaching social studies, religion, and culture lessons that were transcribed using speech recognition technology and then turned into text. Additionally, data from the Wisesight Sentiment Analysis database was utilised.Three categories of machine learning models were used by the researchers: Group 1 consists of four models that preprocess data before analysis and categorise teacher emotions using machine learning techniques; Group 2 consists of four models that extract features from text using autoencoders and categorise teacher emotions using deep learning techniques; and Group 3 consists of two models that preprocess data before analysis and categorise teacher emotions using deep learning techniques.By using TF-IDF (Term Frequency-Inverse Document Frequency) to extract features from the text, the researchers prepared the text data for analysis. They also used Principal Component Analysis (PCA) to minimise the dimensionality of the feature set. They used the SMOTE approach to address the problem of unbalanced data. All three groups of models also underwent cross-validation hyperparameter tuning.On the basis of accuracy, precision, recall, and F1-score, the models performances were compared in the study. These are the conclusions:1. It was discovered that, out of the three groups, LSTM models with tuned hyperparameters had the highest accuracy for classifying emotions, coming in at 71%. Overall, these models performed well in terms of accuracy and F1-score.2. In Group 1, Support Vector Machine (SVM) models with tuned hyperparameters achieved an accuracy rate of 66% for emotion classification. In Group 2, which combined SVM and Logistic Regression models with tuned hyperparameters for feature extraction, both models demonstrated similar performance with an approximate accuracy rate of 68%. In Group 3, LSTM models outperformed other models with the highest accuracy rate of 71%.