Streamlined Teacher Evaluations: Leveraging ML with SMOTE and Streamlit Approach for Real-Time Sentiment Analysis of Student Feedback
Keywords:
Student feedback analytics, NLP - Natural Language Processing, ML - Machine Learning, sentiment prediction, SVM - support vector machine, SMOTE – synthetic minority over-sampling technique, Streamlit ApplicationAbstract
In this research, we collect student feedback on teachers through Google Forms using open-ended questions, allowing students to freely express their feelings, opinions, thoughts, and evaluations of teaching styles and pedagogies. The collected textual data is analyzed using the NLP – a natural language processing technique to predict the student feedback sentiments. We employ a Machine Learning (ML) model, specifically a Support Vector Machine (SVM) enhanced with SMOTE to address data imbalance. After rigorous evaluation and validation, the SVM model, achieving 86% accuracy, was selected for sentiment prediction. This pre-trained model is deployed through a Streamlit application, designed with HTML, CSS, and Python. The novelty of this research lies in the Streamlit app, enabling students to submit feedback and receive sentiment analysis results in real-time. This system aids in continuous feedback collection, facilitating daily, weekly, monthly, and semester-wise analysis. Such insights are valuable for teachers to understand student satisfaction, for higher authorities to monitor overall performance, and for HR during the appraisal process. Additionally, this feedback mechanism supports the NAAC accreditation process, making feedback collection timely and efficient. With 75% of the implementation completed, the remaining will be finalized soon, enhancing the system's capability to forecast feedback trends and support decision-making processes
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